%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Welcome to the Amazon Web Services Clean Rooms ML API Reference. %% %% Amazon Web Services Clean Rooms ML provides a privacy-enhancing method for %% two parties to identify similar users in their data without the need to %% share their data with each other. The first party brings the training data %% to Clean Rooms so that they can create and configure an audience model %% (lookalike model) and associate it with a collaboration. The second party %% then brings their seed data to Clean Rooms and generates an audience %% (lookalike segment) that resembles the training data. %% %% To learn more about Amazon Web Services Clean Rooms ML concepts, %% procedures, and best practices, see the Clean Rooms User Guide: %% https://docs.aws.amazon.com/clean-rooms/latest/userguide/machine-learning.html. %% %% To learn more about SQL commands, functions, and conditions supported in %% Clean Rooms, see the Clean Rooms SQL Reference: %% https://docs.aws.amazon.com/clean-rooms/latest/sql-reference/sql-reference.html. -module(aws_cleanroomsml). -export([cancel_trained_model/4, cancel_trained_model/5, cancel_trained_model_inference_job/4, cancel_trained_model_inference_job/5, create_audience_model/2, create_audience_model/3, create_configured_audience_model/2, create_configured_audience_model/3, create_configured_model_algorithm/2, create_configured_model_algorithm/3, create_configured_model_algorithm_association/3, create_configured_model_algorithm_association/4, create_ml_input_channel/3, create_ml_input_channel/4, create_trained_model/3, create_trained_model/4, create_training_dataset/2, create_training_dataset/3, delete_audience_generation_job/3, delete_audience_generation_job/4, delete_audience_model/3, delete_audience_model/4, delete_configured_audience_model/3, delete_configured_audience_model/4, delete_configured_audience_model_policy/3, delete_configured_audience_model_policy/4, delete_configured_model_algorithm/3, delete_configured_model_algorithm/4, delete_configured_model_algorithm_association/4, delete_configured_model_algorithm_association/5, delete_ml_configuration/3, delete_ml_configuration/4, delete_ml_input_channel_data/4, delete_ml_input_channel_data/5, delete_trained_model_output/4, delete_trained_model_output/5, delete_training_dataset/3, delete_training_dataset/4, get_audience_generation_job/2, get_audience_generation_job/4, get_audience_generation_job/5, get_audience_model/2, get_audience_model/4, get_audience_model/5, get_collaboration_configured_model_algorithm_association/3, get_collaboration_configured_model_algorithm_association/5, get_collaboration_configured_model_algorithm_association/6, get_collaboration_ml_input_channel/3, get_collaboration_ml_input_channel/5, get_collaboration_ml_input_channel/6, get_collaboration_trained_model/3, get_collaboration_trained_model/5, get_collaboration_trained_model/6, get_configured_audience_model/2, get_configured_audience_model/4, get_configured_audience_model/5, get_configured_audience_model_policy/2, get_configured_audience_model_policy/4, get_configured_audience_model_policy/5, get_configured_model_algorithm/2, get_configured_model_algorithm/4, get_configured_model_algorithm/5, get_configured_model_algorithm_association/3, get_configured_model_algorithm_association/5, get_configured_model_algorithm_association/6, get_ml_configuration/2, get_ml_configuration/4, get_ml_configuration/5, get_ml_input_channel/3, get_ml_input_channel/5, get_ml_input_channel/6, get_trained_model/3, get_trained_model/5, get_trained_model/6, get_trained_model_inference_job/3, get_trained_model_inference_job/5, get_trained_model_inference_job/6, get_training_dataset/2, get_training_dataset/4, get_training_dataset/5, list_audience_export_jobs/1, list_audience_export_jobs/3, list_audience_export_jobs/4, list_audience_generation_jobs/1, list_audience_generation_jobs/3, list_audience_generation_jobs/4, list_audience_models/1, list_audience_models/3, list_audience_models/4, list_collaboration_configured_model_algorithm_associations/2, list_collaboration_configured_model_algorithm_associations/4, list_collaboration_configured_model_algorithm_associations/5, list_collaboration_ml_input_channels/2, list_collaboration_ml_input_channels/4, list_collaboration_ml_input_channels/5, list_collaboration_trained_model_export_jobs/3, list_collaboration_trained_model_export_jobs/5, list_collaboration_trained_model_export_jobs/6, list_collaboration_trained_model_inference_jobs/2, list_collaboration_trained_model_inference_jobs/4, list_collaboration_trained_model_inference_jobs/5, list_collaboration_trained_models/2, list_collaboration_trained_models/4, list_collaboration_trained_models/5, list_configured_audience_models/1, list_configured_audience_models/3, list_configured_audience_models/4, list_configured_model_algorithm_associations/2, list_configured_model_algorithm_associations/4, list_configured_model_algorithm_associations/5, list_configured_model_algorithms/1, list_configured_model_algorithms/3, list_configured_model_algorithms/4, list_ml_input_channels/2, list_ml_input_channels/4, list_ml_input_channels/5, list_tags_for_resource/2, list_tags_for_resource/4, list_tags_for_resource/5, list_trained_model_inference_jobs/2, list_trained_model_inference_jobs/4, list_trained_model_inference_jobs/5, list_trained_model_versions/3, list_trained_model_versions/5, list_trained_model_versions/6, list_trained_models/2, list_trained_models/4, list_trained_models/5, list_training_datasets/1, list_training_datasets/3, list_training_datasets/4, put_configured_audience_model_policy/3, put_configured_audience_model_policy/4, put_ml_configuration/3, put_ml_configuration/4, start_audience_export_job/2, start_audience_export_job/3, start_audience_generation_job/2, start_audience_generation_job/3, start_trained_model_export_job/4, start_trained_model_export_job/5, start_trained_model_inference_job/3, start_trained_model_inference_job/4, tag_resource/3, tag_resource/4, untag_resource/3, untag_resource/4, update_configured_audience_model/3, update_configured_audience_model/4]). -include_lib("hackney/include/hackney_lib.hrl"). %% Example: %% update_configured_audience_model_response() :: #{ %% <<"configuredAudienceModelArn">> => string() %% } -type update_configured_audience_model_response() :: #{binary() => any()}. %% Example: %% list_trained_model_inference_jobs_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"trainedModelArn">> => string(), %% <<"trainedModelVersionIdentifier">> => string() %% } -type list_trained_model_inference_jobs_request() :: #{binary() => any()}. %% Example: %% trained_model_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"incrementalTrainingDataChannels">> => list(incremental_training_data_channel_output()), %% <<"membershipIdentifier">> => string(), %% <<"name">> => string(), %% <<"status">> => list(any()), %% <<"trainedModelArn">> => string(), %% <<"updateTime">> => [non_neg_integer()], %% <<"versionIdentifier">> => string() %% } -type trained_model_summary() :: #{binary() => any()}. %% Example: %% start_trained_model_inference_job_response() :: #{ %% <<"trainedModelInferenceJobArn">> => string() %% } -type start_trained_model_inference_job_response() :: #{binary() => any()}. %% Example: %% list_configured_model_algorithm_associations_response() :: #{ %% <<"configuredModelAlgorithmAssociations">> => list(configured_model_algorithm_association_summary()), %% <<"nextToken">> => string() %% } -type list_configured_model_algorithm_associations_response() :: #{binary() => any()}. %% Example: %% create_audience_model_response() :: #{ %% <<"audienceModelArn">> => string() %% } -type create_audience_model_response() :: #{binary() => any()}. %% Example: %% tag_resource_request() :: #{ %% <<"tags">> := map() %% } -type tag_resource_request() :: #{binary() => any()}. %% Example: %% audience_quality_metrics() :: #{ %% <<"recallMetric">> => [float()], %% <<"relevanceMetrics">> => list(relevance_metric()) %% } -type audience_quality_metrics() :: #{binary() => any()}. %% Example: %% start_audience_generation_job_response() :: #{ %% <<"audienceGenerationJobArn">> => string() %% } -type start_audience_generation_job_response() :: #{binary() => any()}. %% Example: %% trained_model_exports_max_size() :: #{ %% <<"unit">> => list(any()), %% <<"value">> => float() %% } -type trained_model_exports_max_size() :: #{binary() => any()}. %% Example: %% get_configured_model_algorithm_association_response() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmArn">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"name">> => string(), %% <<"privacyConfiguration">> => privacy_configuration(), %% <<"tags">> => map(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_configured_model_algorithm_association_response() :: #{binary() => any()}. %% Example: %% get_audience_generation_job_response() :: #{ %% <<"audienceGenerationJobArn">> => string(), %% <<"collaborationId">> => string(), %% <<"configuredAudienceModelArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"includeSeedInOutput">> => [boolean()], %% <<"metrics">> => audience_quality_metrics(), %% <<"name">> => string(), %% <<"protectedQueryIdentifier">> => [string()], %% <<"seedAudience">> => audience_generation_job_data_source(), %% <<"startedBy">> => string(), %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"tags">> => map(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_audience_generation_job_response() :: #{binary() => any()}. %% Example: %% list_configured_model_algorithms_response() :: #{ %% <<"configuredModelAlgorithms">> => list(configured_model_algorithm_summary()), %% <<"nextToken">> => string() %% } -type list_configured_model_algorithms_response() :: #{binary() => any()}. %% Example: %% create_ml_input_channel_request() :: #{ %% <<"configuredModelAlgorithmAssociations">> := list(string()), %% <<"description">> => string(), %% <<"inputChannel">> := input_channel(), %% <<"kmsKeyArn">> => string(), %% <<"name">> := string(), %% <<"retentionInDays">> := [integer()], %% <<"tags">> => map() %% } -type create_ml_input_channel_request() :: #{binary() => any()}. %% Example: %% get_trained_model_request() :: #{ %% <<"versionIdentifier">> => string() %% } -type get_trained_model_request() :: #{binary() => any()}. %% Example: %% access_budget() :: #{ %% <<"aggregateRemainingBudget">> => integer(), %% <<"details">> => list(access_budget_details()), %% <<"resourceArn">> => string() %% } -type access_budget() :: #{binary() => any()}. %% Example: %% log_redaction_configuration() :: #{ %% <<"customEntityConfig">> => custom_entity_config(), %% <<"entitiesToRedact">> => list(list(any())()) %% } -type log_redaction_configuration() :: #{binary() => any()}. %% Example: %% get_training_dataset_response() :: #{ %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"name">> => string(), %% <<"roleArn">> => string(), %% <<"status">> => list(any()), %% <<"tags">> => map(), %% <<"trainingData">> => list(dataset()), %% <<"trainingDatasetArn">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_training_dataset_response() :: #{binary() => any()}. %% Example: %% trained_models_configuration_policy() :: #{ %% <<"containerLogs">> => list(logs_configuration_policy()), %% <<"containerMetrics">> => metrics_configuration_policy(), %% <<"maxArtifactSize">> => trained_model_artifact_max_size() %% } -type trained_models_configuration_policy() :: #{binary() => any()}. %% Example: %% trained_model_inference_job_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"logsStatus">> => list(any()), %% <<"logsStatusDetails">> => [string()], %% <<"membershipIdentifier">> => string(), %% <<"metricsStatus">> => list(any()), %% <<"metricsStatusDetails">> => [string()], %% <<"name">> => string(), %% <<"outputConfiguration">> => inference_output_configuration(), %% <<"status">> => list(any()), %% <<"trainedModelArn">> => string(), %% <<"trainedModelInferenceJobArn">> => string(), %% <<"trainedModelVersionIdentifier">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type trained_model_inference_job_summary() :: #{binary() => any()}. %% Example: %% create_configured_audience_model_response() :: #{ %% <<"configuredAudienceModelArn">> => string() %% } -type create_configured_audience_model_response() :: #{binary() => any()}. %% Example: %% untag_resource_response() :: #{} -type untag_resource_response() :: #{}. %% Example: %% trained_model_export_receiver_member() :: #{ %% <<"accountId">> => string() %% } -type trained_model_export_receiver_member() :: #{binary() => any()}. %% Example: %% inference_output_configuration() :: #{ %% <<"accept">> => [string()], %% <<"members">> => list(inference_receiver_member()) %% } -type inference_output_configuration() :: #{binary() => any()}. %% Example: %% protected_query_input_parameters() :: #{ %% <<"computeConfiguration">> => list(), %% <<"resultFormat">> => list(any()), %% <<"sqlParameters">> => protected_query_s_q_l_parameters() %% } -type protected_query_input_parameters() :: #{binary() => any()}. %% Example: %% list_configured_audience_models_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_configured_audience_models_request() :: #{binary() => any()}. %% Example: %% list_training_datasets_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_training_datasets_request() :: #{binary() => any()}. %% Example: %% get_configured_model_algorithm_association_request() :: #{} -type get_configured_model_algorithm_association_request() :: #{}. %% Example: %% create_configured_model_algorithm_association_request() :: #{ %% <<"configuredModelAlgorithmArn">> := string(), %% <<"description">> => string(), %% <<"name">> := string(), %% <<"privacyConfiguration">> => privacy_configuration(), %% <<"tags">> => map() %% } -type create_configured_model_algorithm_association_request() :: #{binary() => any()}. %% Example: %% put_ml_configuration_request() :: #{ %% <<"defaultOutputLocation">> := ml_output_configuration() %% } -type put_ml_configuration_request() :: #{binary() => any()}. %% Example: %% get_training_dataset_request() :: #{} -type get_training_dataset_request() :: #{}. %% Example: %% get_ml_input_channel_response() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociations">> => list(string()), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"inputChannel">> => input_channel(), %% <<"kmsKeyArn">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"mlInputChannelArn">> => string(), %% <<"name">> => string(), %% <<"numberOfFiles">> => [float()], %% <<"numberOfRecords">> => [float()], %% <<"privacyBudgets">> => list(), %% <<"protectedQueryIdentifier">> => string(), %% <<"retentionInDays">> => [integer()], %% <<"sizeInGb">> => [float()], %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"syntheticDataConfiguration">> => synthetic_data_configuration(), %% <<"tags">> => map(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_ml_input_channel_response() :: #{binary() => any()}. %% Example: %% get_ml_configuration_request() :: #{} -type get_ml_configuration_request() :: #{}. %% Example: %% list_ml_input_channels_response() :: #{ %% <<"mlInputChannelsList">> => list(ml_input_channel_summary()), %% <<"nextToken">> => string() %% } -type list_ml_input_channels_response() :: #{binary() => any()}. %% Example: %% relevance_metric() :: #{ %% <<"audienceSize">> => audience_size(), %% <<"score">> => [float()] %% } -type relevance_metric() :: #{binary() => any()}. %% Example: %% get_collaboration_trained_model_response() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"creatorAccountId">> => string(), %% <<"description">> => string(), %% <<"incrementalTrainingDataChannels">> => list(incremental_training_data_channel_output()), %% <<"logsStatus">> => list(any()), %% <<"logsStatusDetails">> => [string()], %% <<"membershipIdentifier">> => string(), %% <<"metricsStatus">> => list(any()), %% <<"metricsStatusDetails">> => [string()], %% <<"name">> => string(), %% <<"resourceConfig">> => resource_config(), %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"stoppingCondition">> => stopping_condition(), %% <<"trainedModelArn">> => string(), %% <<"trainingContainerImageDigest">> => [string()], %% <<"trainingInputMode">> => list(any()), %% <<"updateTime">> => [non_neg_integer()], %% <<"versionIdentifier">> => string() %% } -type get_collaboration_trained_model_response() :: #{binary() => any()}. %% Example: %% create_configured_audience_model_request() :: #{ %% <<"audienceModelArn">> := string(), %% <<"audienceSizeConfig">> => audience_size_config(), %% <<"childResourceTagOnCreatePolicy">> => list(any()), %% <<"description">> => string(), %% <<"minMatchingSeedSize">> => integer(), %% <<"name">> := string(), %% <<"outputConfig">> := configured_audience_model_output_config(), %% <<"sharedAudienceMetrics">> := list(list(any())()), %% <<"tags">> => map() %% } -type create_configured_audience_model_request() :: #{binary() => any()}. %% Example: %% collaboration_ml_input_channel_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociations">> => list(string()), %% <<"createTime">> => [non_neg_integer()], %% <<"creatorAccountId">> => string(), %% <<"description">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"mlInputChannelArn">> => string(), %% <<"name">> => string(), %% <<"status">> => list(any()), %% <<"updateTime">> => [non_neg_integer()] %% } -type collaboration_ml_input_channel_summary() :: #{binary() => any()}. %% Example: %% ml_input_channel_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociations">> => list(string()), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"mlInputChannelArn">> => string(), %% <<"name">> => string(), %% <<"protectedQueryIdentifier">> => string(), %% <<"status">> => list(any()), %% <<"updateTime">> => [non_neg_integer()] %% } -type ml_input_channel_summary() :: #{binary() => any()}. %% Example: %% create_configured_model_algorithm_association_response() :: #{ %% <<"configuredModelAlgorithmAssociationArn">> => string() %% } -type create_configured_model_algorithm_association_response() :: #{binary() => any()}. %% Example: %% metric_definition() :: #{ %% <<"name">> => string(), %% <<"regex">> => string() %% } -type metric_definition() :: #{binary() => any()}. %% Example: %% delete_configured_audience_model_request() :: #{} -type delete_configured_audience_model_request() :: #{}. %% Example: %% get_ml_input_channel_request() :: #{} -type get_ml_input_channel_request() :: #{}. %% Example: %% list_collaboration_trained_model_export_jobs_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"trainedModelVersionIdentifier">> => string() %% } -type list_collaboration_trained_model_export_jobs_request() :: #{binary() => any()}. %% Example: %% internal_service_exception() :: #{ %% <<"message">> => [string()] %% } -type internal_service_exception() :: #{binary() => any()}. %% Example: %% list_collaboration_configured_model_algorithm_associations_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_collaboration_configured_model_algorithm_associations_request() :: #{binary() => any()}. %% Example: %% get_trained_model_response() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"dataChannels">> => list(model_training_data_channel()), %% <<"description">> => string(), %% <<"environment">> => map(), %% <<"hyperparameters">> => map(), %% <<"incrementalTrainingDataChannels">> => list(incremental_training_data_channel_output()), %% <<"kmsKeyArn">> => string(), %% <<"logsStatus">> => list(any()), %% <<"logsStatusDetails">> => [string()], %% <<"membershipIdentifier">> => string(), %% <<"metricsStatus">> => list(any()), %% <<"metricsStatusDetails">> => [string()], %% <<"name">> => string(), %% <<"resourceConfig">> => resource_config(), %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"stoppingCondition">> => stopping_condition(), %% <<"tags">> => map(), %% <<"trainedModelArn">> => string(), %% <<"trainingContainerImageDigest">> => [string()], %% <<"trainingInputMode">> => list(any()), %% <<"updateTime">> => [non_neg_integer()], %% <<"versionIdentifier">> => string() %% } -type get_trained_model_response() :: #{binary() => any()}. %% Example: %% untag_resource_request() :: #{ %% <<"tagKeys">> := list(string()) %% } -type untag_resource_request() :: #{binary() => any()}. %% Example: %% collaboration_trained_model_export_job_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"creatorAccountId">> => string(), %% <<"description">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"name">> => string(), %% <<"outputConfiguration">> => trained_model_export_output_configuration(), %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"trainedModelArn">> => string(), %% <<"trainedModelVersionIdentifier">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type collaboration_trained_model_export_job_summary() :: #{binary() => any()}. %% Example: %% list_trained_model_versions_response() :: #{ %% <<"nextToken">> => string(), %% <<"trainedModels">> => list(trained_model_summary()) %% } -type list_trained_model_versions_response() :: #{binary() => any()}. %% Example: %% status_details() :: #{ %% <<"message">> => [string()], %% <<"statusCode">> => [string()] %% } -type status_details() :: #{binary() => any()}. %% Example: %% start_audience_export_job_request() :: #{ %% <<"audienceGenerationJobArn">> := string(), %% <<"audienceSize">> := audience_size(), %% <<"description">> => string(), %% <<"name">> := string() %% } -type start_audience_export_job_request() :: #{binary() => any()}. %% Example: %% delete_training_dataset_request() :: #{} -type delete_training_dataset_request() :: #{}. %% Example: %% list_ml_input_channels_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_ml_input_channels_request() :: #{binary() => any()}. %% Example: %% create_configured_model_algorithm_request() :: #{ %% <<"description">> => string(), %% <<"inferenceContainerConfig">> => inference_container_config(), %% <<"kmsKeyArn">> => string(), %% <<"name">> := string(), %% <<"roleArn">> := string(), %% <<"tags">> => map(), %% <<"trainingContainerConfig">> => container_config() %% } -type create_configured_model_algorithm_request() :: #{binary() => any()}. %% Example: %% list_audience_models_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_audience_models_request() :: #{binary() => any()}. %% Example: %% list_collaboration_ml_input_channels_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_collaboration_ml_input_channels_request() :: #{binary() => any()}. %% Example: %% dataset() :: #{ %% <<"inputConfig">> => dataset_input_config(), %% <<"type">> => list(any()) %% } -type dataset() :: #{binary() => any()}. %% Example: %% metrics_configuration_policy() :: #{ %% <<"noiseLevel">> => list(any()) %% } -type metrics_configuration_policy() :: #{binary() => any()}. %% Example: %% data_privacy_scores() :: #{ %% <<"membershipInferenceAttackScores">> => list(membership_inference_attack_score()) %% } -type data_privacy_scores() :: #{binary() => any()}. %% Example: %% custom_entity_config() :: #{ %% <<"customDataIdentifiers">> => list(string()) %% } -type custom_entity_config() :: #{binary() => any()}. %% Example: %% audience_size() :: #{ %% <<"type">> => list(any()), %% <<"value">> => integer() %% } -type audience_size() :: #{binary() => any()}. %% Example: %% start_audience_generation_job_request() :: #{ %% <<"collaborationId">> => string(), %% <<"configuredAudienceModelArn">> := string(), %% <<"description">> => string(), %% <<"includeSeedInOutput">> => [boolean()], %% <<"name">> := string(), %% <<"seedAudience">> := audience_generation_job_data_source(), %% <<"tags">> => map() %% } -type start_audience_generation_job_request() :: #{binary() => any()}. %% Example: %% model_training_data_channel() :: #{ %% <<"channelName">> => string(), %% <<"mlInputChannelArn">> => string(), %% <<"s3DataDistributionType">> => list(any()) %% } -type model_training_data_channel() :: #{binary() => any()}. %% Example: %% audience_generation_job_summary() :: #{ %% <<"audienceGenerationJobArn">> => string(), %% <<"collaborationId">> => string(), %% <<"configuredAudienceModelArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"name">> => string(), %% <<"startedBy">> => string(), %% <<"status">> => list(any()), %% <<"updateTime">> => [non_neg_integer()] %% } -type audience_generation_job_summary() :: #{binary() => any()}. %% Example: %% trained_model_inference_jobs_configuration_policy() :: #{ %% <<"containerLogs">> => list(logs_configuration_policy()), %% <<"maxOutputSize">> => trained_model_inference_max_output_size() %% } -type trained_model_inference_jobs_configuration_policy() :: #{binary() => any()}. %% Example: %% conflict_exception() :: #{ %% <<"message">> => [string()] %% } -type conflict_exception() :: #{binary() => any()}. %% Example: %% resource_not_found_exception() :: #{ %% <<"message">> => [string()] %% } -type resource_not_found_exception() :: #{binary() => any()}. %% Example: %% container_config() :: #{ %% <<"arguments">> => list(string()), %% <<"entrypoint">> => list(string()), %% <<"imageUri">> => string(), %% <<"metricDefinitions">> => list(metric_definition()) %% } -type container_config() :: #{binary() => any()}. %% Example: %% get_audience_model_request() :: #{} -type get_audience_model_request() :: #{}. %% Example: %% list_trained_model_inference_jobs_response() :: #{ %% <<"nextToken">> => string(), %% <<"trainedModelInferenceJobs">> => list(trained_model_inference_job_summary()) %% } -type list_trained_model_inference_jobs_response() :: #{binary() => any()}. %% Example: %% configured_audience_model_output_config() :: #{ %% <<"destination">> => audience_destination(), %% <<"roleArn">> => string() %% } -type configured_audience_model_output_config() :: #{binary() => any()}. %% Example: %% get_collaboration_configured_model_algorithm_association_request() :: #{} -type get_collaboration_configured_model_algorithm_association_request() :: #{}. %% Example: %% configured_model_algorithm_summary() :: #{ %% <<"configuredModelAlgorithmArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"name">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type configured_model_algorithm_summary() :: #{binary() => any()}. %% Example: %% incremental_training_data_channel() :: #{ %% <<"channelName">> => string(), %% <<"trainedModelArn">> => string(), %% <<"versionIdentifier">> => string() %% } -type incremental_training_data_channel() :: #{binary() => any()}. %% Example: %% trained_model_inference_max_output_size() :: #{ %% <<"unit">> => list(any()), %% <<"value">> => float() %% } -type trained_model_inference_max_output_size() :: #{binary() => any()}. %% Example: %% get_collaboration_trained_model_request() :: #{ %% <<"versionIdentifier">> => string() %% } -type get_collaboration_trained_model_request() :: #{binary() => any()}. %% Example: %% inference_resource_config() :: #{ %% <<"instanceCount">> => [integer()], %% <<"instanceType">> => list(any()) %% } -type inference_resource_config() :: #{binary() => any()}. %% Example: %% service_quota_exceeded_exception() :: #{ %% <<"message">> => [string()], %% <<"quotaName">> => [string()], %% <<"quotaValue">> => [float()] %% } -type service_quota_exceeded_exception() :: #{binary() => any()}. %% Example: %% get_configured_audience_model_response() :: #{ %% <<"audienceModelArn">> => string(), %% <<"audienceSizeConfig">> => audience_size_config(), %% <<"childResourceTagOnCreatePolicy">> => list(any()), %% <<"configuredAudienceModelArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"minMatchingSeedSize">> => integer(), %% <<"name">> => string(), %% <<"outputConfig">> => configured_audience_model_output_config(), %% <<"sharedAudienceMetrics">> => list(list(any())()), %% <<"status">> => list(any()), %% <<"tags">> => map(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_configured_audience_model_response() :: #{binary() => any()}. %% Example: %% audience_model_summary() :: #{ %% <<"audienceModelArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"name">> => string(), %% <<"status">> => list(any()), %% <<"trainingDatasetArn">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type audience_model_summary() :: #{binary() => any()}. %% Example: %% ml_output_configuration() :: #{ %% <<"destination">> => destination(), %% <<"roleArn">> => string() %% } -type ml_output_configuration() :: #{binary() => any()}. %% Example: %% delete_audience_generation_job_request() :: #{} -type delete_audience_generation_job_request() :: #{}. %% Example: %% get_ml_configuration_response() :: #{ %% <<"createTime">> => [non_neg_integer()], %% <<"defaultOutputLocation">> => ml_output_configuration(), %% <<"membershipIdentifier">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_ml_configuration_response() :: #{binary() => any()}. %% Example: %% stopping_condition() :: #{ %% <<"maxRuntimeInSeconds">> => [integer()] %% } -type stopping_condition() :: #{binary() => any()}. %% Example: %% list_collaboration_trained_model_export_jobs_response() :: #{ %% <<"collaborationTrainedModelExportJobs">> => list(collaboration_trained_model_export_job_summary()), %% <<"nextToken">> => string() %% } -type list_collaboration_trained_model_export_jobs_response() :: #{binary() => any()}. %% Example: %% create_trained_model_request() :: #{ %% <<"configuredModelAlgorithmAssociationArn">> := string(), %% <<"dataChannels">> := list(model_training_data_channel()), %% <<"description">> => string(), %% <<"environment">> => map(), %% <<"hyperparameters">> => map(), %% <<"incrementalTrainingDataChannels">> => list(incremental_training_data_channel()), %% <<"kmsKeyArn">> => string(), %% <<"name">> := string(), %% <<"resourceConfig">> := resource_config(), %% <<"stoppingCondition">> => stopping_condition(), %% <<"tags">> => map(), %% <<"trainingInputMode">> => list(any()) %% } -type create_trained_model_request() :: #{binary() => any()}. %% Example: %% list_training_datasets_response() :: #{ %% <<"nextToken">> => string(), %% <<"trainingDatasets">> => list(training_dataset_summary()) %% } -type list_training_datasets_response() :: #{binary() => any()}. %% Example: %% collaboration_trained_model_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"creatorAccountId">> => string(), %% <<"description">> => string(), %% <<"incrementalTrainingDataChannels">> => list(incremental_training_data_channel_output()), %% <<"membershipIdentifier">> => string(), %% <<"name">> => string(), %% <<"status">> => list(any()), %% <<"trainedModelArn">> => string(), %% <<"updateTime">> => [non_neg_integer()], %% <<"versionIdentifier">> => string() %% } -type collaboration_trained_model_summary() :: #{binary() => any()}. %% Example: %% list_tags_for_resource_response() :: #{ %% <<"tags">> => map() %% } -type list_tags_for_resource_response() :: #{binary() => any()}. %% Example: %% inference_container_config() :: #{ %% <<"imageUri">> => string() %% } -type inference_container_config() :: #{binary() => any()}. %% Example: %% list_configured_model_algorithms_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_configured_model_algorithms_request() :: #{binary() => any()}. %% Example: %% ml_synthetic_data_parameters() :: #{ %% <<"columnClassification">> => column_classification_details(), %% <<"epsilon">> => [float()], %% <<"maxMembershipInferenceAttackScore">> => [float()] %% } -type ml_synthetic_data_parameters() :: #{binary() => any()}. %% Example: %% configured_model_algorithm_association_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmArn">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"name">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type configured_model_algorithm_association_summary() :: #{binary() => any()}. %% Example: %% audience_export_job_summary() :: #{ %% <<"audienceGenerationJobArn">> => string(), %% <<"audienceSize">> => audience_size(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"name">> => string(), %% <<"outputLocation">> => string(), %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"updateTime">> => [non_neg_integer()] %% } -type audience_export_job_summary() :: #{binary() => any()}. %% Example: %% delete_audience_model_request() :: #{} -type delete_audience_model_request() :: #{}. %% Example: %% trained_model_exports_configuration_policy() :: #{ %% <<"filesToExport">> => list(list(any())()), %% <<"maxSize">> => trained_model_exports_max_size() %% } -type trained_model_exports_configuration_policy() :: #{binary() => any()}. %% Example: %% list_configured_model_algorithm_associations_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_configured_model_algorithm_associations_request() :: #{binary() => any()}. %% Example: %% create_training_dataset_response() :: #{ %% <<"trainingDatasetArn">> => string() %% } -type create_training_dataset_response() :: #{binary() => any()}. %% Example: %% create_audience_model_request() :: #{ %% <<"description">> => string(), %% <<"kmsKeyArn">> => string(), %% <<"name">> := string(), %% <<"tags">> => map(), %% <<"trainingDataEndTime">> => [non_neg_integer()], %% <<"trainingDataStartTime">> => [non_neg_integer()], %% <<"trainingDatasetArn">> := string() %% } -type create_audience_model_request() :: #{binary() => any()}. %% Example: %% privacy_configuration() :: #{ %% <<"policies">> => privacy_configuration_policies() %% } -type privacy_configuration() :: #{binary() => any()}. %% Example: %% start_trained_model_inference_job_request() :: #{ %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"containerExecutionParameters">> => inference_container_execution_parameters(), %% <<"dataSource">> := model_inference_data_source(), %% <<"description">> => string(), %% <<"environment">> => map(), %% <<"kmsKeyArn">> => string(), %% <<"name">> := string(), %% <<"outputConfiguration">> := inference_output_configuration(), %% <<"resourceConfig">> := inference_resource_config(), %% <<"tags">> => map(), %% <<"trainedModelArn">> := string(), %% <<"trainedModelVersionIdentifier">> => string() %% } -type start_trained_model_inference_job_request() :: #{binary() => any()}. %% Example: %% trained_model_artifact_max_size() :: #{ %% <<"unit">> => list(any()), %% <<"value">> => float() %% } -type trained_model_artifact_max_size() :: #{binary() => any()}. %% Example: %% inference_receiver_member() :: #{ %% <<"accountId">> => string() %% } -type inference_receiver_member() :: #{binary() => any()}. %% Example: %% synthetic_data_column_properties() :: #{ %% <<"columnName">> => string(), %% <<"columnType">> => list(any()), %% <<"isPredictiveValue">> => [boolean()] %% } -type synthetic_data_column_properties() :: #{binary() => any()}. %% Example: %% collaboration_configured_model_algorithm_association_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmArn">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"creatorAccountId">> => string(), %% <<"description">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"name">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type collaboration_configured_model_algorithm_association_summary() :: #{binary() => any()}. %% Example: %% create_ml_input_channel_response() :: #{ %% <<"mlInputChannelArn">> => string() %% } -type create_ml_input_channel_response() :: #{binary() => any()}. %% Example: %% get_trained_model_inference_job_request() :: #{} -type get_trained_model_inference_job_request() :: #{}. %% Example: %% list_configured_audience_models_response() :: #{ %% <<"configuredAudienceModels">> => list(configured_audience_model_summary()), %% <<"nextToken">> => string() %% } -type list_configured_audience_models_response() :: #{binary() => any()}. %% Example: %% column_classification_details() :: #{ %% <<"columnMapping">> => list(synthetic_data_column_properties()) %% } -type column_classification_details() :: #{binary() => any()}. %% Example: %% resource_config() :: #{ %% <<"instanceCount">> => [integer()], %% <<"instanceType">> => list(any()), %% <<"volumeSizeInGB">> => [integer()] %% } -type resource_config() :: #{binary() => any()}. %% Example: %% get_collaboration_configured_model_algorithm_association_response() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmArn">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"creatorAccountId">> => string(), %% <<"description">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"name">> => string(), %% <<"privacyConfiguration">> => privacy_configuration(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_collaboration_configured_model_algorithm_association_response() :: #{binary() => any()}. %% Example: %% s3_config_map() :: #{ %% <<"s3Uri">> => string() %% } -type s3_config_map() :: #{binary() => any()}. %% Example: %% access_denied_exception() :: #{ %% <<"message">> => [string()] %% } -type access_denied_exception() :: #{binary() => any()}. %% Example: %% list_audience_export_jobs_response() :: #{ %% <<"audienceExportJobs">> => list(audience_export_job_summary()), %% <<"nextToken">> => string() %% } -type list_audience_export_jobs_response() :: #{binary() => any()}. %% Example: %% incremental_training_data_channel_output() :: #{ %% <<"channelName">> => string(), %% <<"modelName">> => string(), %% <<"versionIdentifier">> => string() %% } -type incremental_training_data_channel_output() :: #{binary() => any()}. %% Example: %% delete_ml_input_channel_data_request() :: #{} -type delete_ml_input_channel_data_request() :: #{}. %% Example: %% list_trained_models_response() :: #{ %% <<"nextToken">> => string(), %% <<"trainedModels">> => list(trained_model_summary()) %% } -type list_trained_models_response() :: #{binary() => any()}. %% Example: %% tag_resource_response() :: #{} -type tag_resource_response() :: #{}. %% Example: %% synthetic_data_evaluation_scores() :: #{ %% <<"dataPrivacyScores">> => data_privacy_scores() %% } -type synthetic_data_evaluation_scores() :: #{binary() => any()}. %% Example: %% logs_configuration_policy() :: #{ %% <<"allowedAccountIds">> => list([string()]()), %% <<"filterPattern">> => [string()], %% <<"logRedactionConfiguration">> => log_redaction_configuration(), %% <<"logType">> => list(any()) %% } -type logs_configuration_policy() :: #{binary() => any()}. %% Example: %% create_trained_model_response() :: #{ %% <<"trainedModelArn">> => string(), %% <<"versionIdentifier">> => string() %% } -type create_trained_model_response() :: #{binary() => any()}. %% Example: %% start_trained_model_export_job_request() :: #{ %% <<"description">> => string(), %% <<"name">> := string(), %% <<"outputConfiguration">> := trained_model_export_output_configuration(), %% <<"trainedModelVersionIdentifier">> => string() %% } -type start_trained_model_export_job_request() :: #{binary() => any()}. %% Example: %% worker_compute_configuration() :: #{ %% <<"number">> => [integer()], %% <<"properties">> => list(), %% <<"type">> => list(any()) %% } -type worker_compute_configuration() :: #{binary() => any()}. %% Example: %% get_configured_audience_model_request() :: #{} -type get_configured_audience_model_request() :: #{}. %% Example: %% input_channel() :: #{ %% <<"dataSource">> => list(), %% <<"roleArn">> => string() %% } -type input_channel() :: #{binary() => any()}. %% Example: %% cancel_trained_model_inference_job_request() :: #{} -type cancel_trained_model_inference_job_request() :: #{}. %% Example: %% cancel_trained_model_request() :: #{ %% <<"versionIdentifier">> => string() %% } -type cancel_trained_model_request() :: #{binary() => any()}. %% Example: %% list_trained_model_versions_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"status">> => list(any()) %% } -type list_trained_model_versions_request() :: #{binary() => any()}. %% Example: %% list_collaboration_trained_models_response() :: #{ %% <<"collaborationTrainedModels">> => list(collaboration_trained_model_summary()), %% <<"nextToken">> => string() %% } -type list_collaboration_trained_models_response() :: #{binary() => any()}. %% Example: %% validation_exception() :: #{ %% <<"message">> => [string()] %% } -type validation_exception() :: #{binary() => any()}. %% Example: %% list_tags_for_resource_request() :: #{} -type list_tags_for_resource_request() :: #{}. %% Example: %% configured_audience_model_summary() :: #{ %% <<"audienceModelArn">> => string(), %% <<"configuredAudienceModelArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"name">> => string(), %% <<"outputConfig">> => configured_audience_model_output_config(), %% <<"status">> => list(any()), %% <<"updateTime">> => [non_neg_integer()] %% } -type configured_audience_model_summary() :: #{binary() => any()}. %% Example: %% list_collaboration_trained_models_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_collaboration_trained_models_request() :: #{binary() => any()}. %% Example: %% destination() :: #{ %% <<"s3Destination">> => s3_config_map() %% } -type destination() :: #{binary() => any()}. %% Example: %% get_configured_audience_model_policy_response() :: #{ %% <<"configuredAudienceModelArn">> => string(), %% <<"configuredAudienceModelPolicy">> => string(), %% <<"policyHash">> => string() %% } -type get_configured_audience_model_policy_response() :: #{binary() => any()}. %% Example: %% list_collaboration_ml_input_channels_response() :: #{ %% <<"collaborationMLInputChannelsList">> => list(collaboration_ml_input_channel_summary()), %% <<"nextToken">> => string() %% } -type list_collaboration_ml_input_channels_response() :: #{binary() => any()}. %% Example: %% privacy_configuration_policies() :: #{ %% <<"trainedModelExports">> => trained_model_exports_configuration_policy(), %% <<"trainedModelInferenceJobs">> => trained_model_inference_jobs_configuration_policy(), %% <<"trainedModels">> => trained_models_configuration_policy() %% } -type privacy_configuration_policies() :: #{binary() => any()}. %% Example: %% throttling_exception() :: #{ %% <<"message">> => [string()] %% } -type throttling_exception() :: #{binary() => any()}. %% Example: %% glue_data_source() :: #{ %% <<"catalogId">> => string(), %% <<"databaseName">> => string(), %% <<"tableName">> => string() %% } -type glue_data_source() :: #{binary() => any()}. %% Example: %% protected_query_s_q_l_parameters() :: #{ %% <<"analysisTemplateArn">> => string(), %% <<"parameters">> => map(), %% <<"queryString">> => [string()] %% } -type protected_query_s_q_l_parameters() :: #{binary() => any()}. %% Example: %% collaboration_trained_model_inference_job_summary() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"creatorAccountId">> => string(), %% <<"description">> => string(), %% <<"logsStatus">> => list(any()), %% <<"logsStatusDetails">> => [string()], %% <<"membershipIdentifier">> => string(), %% <<"metricsStatus">> => list(any()), %% <<"metricsStatusDetails">> => [string()], %% <<"name">> => string(), %% <<"outputConfiguration">> => inference_output_configuration(), %% <<"status">> => list(any()), %% <<"trainedModelArn">> => string(), %% <<"trainedModelInferenceJobArn">> => string(), %% <<"trainedModelVersionIdentifier">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type collaboration_trained_model_inference_job_summary() :: #{binary() => any()}. %% Example: %% delete_ml_configuration_request() :: #{} -type delete_ml_configuration_request() :: #{}. %% Example: %% put_configured_audience_model_policy_response() :: #{ %% <<"configuredAudienceModelPolicy">> => string(), %% <<"policyHash">> => string() %% } -type put_configured_audience_model_policy_response() :: #{binary() => any()}. %% Example: %% membership_inference_attack_score() :: #{ %% <<"attackVersion">> => list(any()), %% <<"score">> => [float()] %% } -type membership_inference_attack_score() :: #{binary() => any()}. %% Example: %% get_collaboration_ml_input_channel_response() :: #{ %% <<"collaborationIdentifier">> => string(), %% <<"configuredModelAlgorithmAssociations">> => list(string()), %% <<"createTime">> => [non_neg_integer()], %% <<"creatorAccountId">> => string(), %% <<"description">> => string(), %% <<"membershipIdentifier">> => string(), %% <<"mlInputChannelArn">> => string(), %% <<"name">> => string(), %% <<"numberOfRecords">> => [float()], %% <<"privacyBudgets">> => list(), %% <<"retentionInDays">> => [integer()], %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"syntheticDataConfiguration">> => synthetic_data_configuration(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_collaboration_ml_input_channel_response() :: #{binary() => any()}. %% Example: %% get_configured_model_algorithm_request() :: #{} -type get_configured_model_algorithm_request() :: #{}. %% Example: %% model_inference_data_source() :: #{ %% <<"mlInputChannelArn">> => string() %% } -type model_inference_data_source() :: #{binary() => any()}. %% Example: %% create_training_dataset_request() :: #{ %% <<"description">> => string(), %% <<"name">> := string(), %% <<"roleArn">> := string(), %% <<"tags">> => map(), %% <<"trainingData">> := list(dataset()) %% } -type create_training_dataset_request() :: #{binary() => any()}. %% Example: %% delete_configured_model_algorithm_association_request() :: #{} -type delete_configured_model_algorithm_association_request() :: #{}. %% Example: %% training_dataset_summary() :: #{ %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"name">> => string(), %% <<"status">> => list(any()), %% <<"trainingDatasetArn">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type training_dataset_summary() :: #{binary() => any()}. %% Example: %% column_schema() :: #{ %% <<"columnName">> => string(), %% <<"columnTypes">> => list(list(any())()) %% } -type column_schema() :: #{binary() => any()}. %% Example: %% list_collaboration_trained_model_inference_jobs_response() :: #{ %% <<"collaborationTrainedModelInferenceJobs">> => list(collaboration_trained_model_inference_job_summary()), %% <<"nextToken">> => string() %% } -type list_collaboration_trained_model_inference_jobs_response() :: #{binary() => any()}. %% Example: %% get_audience_model_response() :: #{ %% <<"audienceModelArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"kmsKeyArn">> => string(), %% <<"name">> => string(), %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"tags">> => map(), %% <<"trainingDataEndTime">> => [non_neg_integer()], %% <<"trainingDataStartTime">> => [non_neg_integer()], %% <<"trainingDatasetArn">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_audience_model_response() :: #{binary() => any()}. %% Example: %% delete_configured_model_algorithm_request() :: #{} -type delete_configured_model_algorithm_request() :: #{}. %% Example: %% dataset_input_config() :: #{ %% <<"dataSource">> => data_source(), %% <<"schema">> => list(column_schema()) %% } -type dataset_input_config() :: #{binary() => any()}. %% Example: %% create_configured_model_algorithm_response() :: #{ %% <<"configuredModelAlgorithmArn">> => string() %% } -type create_configured_model_algorithm_response() :: #{binary() => any()}. %% Example: %% put_configured_audience_model_policy_request() :: #{ %% <<"configuredAudienceModelPolicy">> := string(), %% <<"policyExistenceCondition">> => list(any()), %% <<"previousPolicyHash">> => string() %% } -type put_configured_audience_model_policy_request() :: #{binary() => any()}. %% Example: %% data_source() :: #{ %% <<"glueDataSource">> => glue_data_source() %% } -type data_source() :: #{binary() => any()}. %% Example: %% audience_size_config() :: #{ %% <<"audienceSizeBins">> => list(integer()), %% <<"audienceSizeType">> => list(any()) %% } -type audience_size_config() :: #{binary() => any()}. %% Example: %% audience_destination() :: #{ %% <<"s3Destination">> => s3_config_map() %% } -type audience_destination() :: #{binary() => any()}. %% Example: %% access_budget_details() :: #{ %% <<"autoRefresh">> => list(any()), %% <<"budget">> => integer(), %% <<"budgetType">> => list(any()), %% <<"endTime">> => [non_neg_integer()], %% <<"remainingBudget">> => integer(), %% <<"startTime">> => [non_neg_integer()] %% } -type access_budget_details() :: #{binary() => any()}. %% Example: %% get_trained_model_inference_job_response() :: #{ %% <<"configuredModelAlgorithmAssociationArn">> => string(), %% <<"containerExecutionParameters">> => inference_container_execution_parameters(), %% <<"createTime">> => [non_neg_integer()], %% <<"dataSource">> => model_inference_data_source(), %% <<"description">> => string(), %% <<"environment">> => map(), %% <<"inferenceContainerImageDigest">> => [string()], %% <<"kmsKeyArn">> => string(), %% <<"logsStatus">> => list(any()), %% <<"logsStatusDetails">> => [string()], %% <<"membershipIdentifier">> => string(), %% <<"metricsStatus">> => list(any()), %% <<"metricsStatusDetails">> => [string()], %% <<"name">> => string(), %% <<"outputConfiguration">> => inference_output_configuration(), %% <<"resourceConfig">> => inference_resource_config(), %% <<"status">> => list(any()), %% <<"statusDetails">> => status_details(), %% <<"tags">> => map(), %% <<"trainedModelArn">> => string(), %% <<"trainedModelInferenceJobArn">> => string(), %% <<"trainedModelVersionIdentifier">> => string(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_trained_model_inference_job_response() :: #{binary() => any()}. %% Example: %% inference_container_execution_parameters() :: #{ %% <<"maxPayloadInMB">> => [integer()] %% } -type inference_container_execution_parameters() :: #{binary() => any()}. %% Example: %% audience_generation_job_data_source() :: #{ %% <<"dataSource">> => s3_config_map(), %% <<"roleArn">> => string(), %% <<"sqlComputeConfiguration">> => list(), %% <<"sqlParameters">> => protected_query_s_q_l_parameters() %% } -type audience_generation_job_data_source() :: #{binary() => any()}. %% Example: %% list_audience_generation_jobs_request() :: #{ %% <<"collaborationId">> => string(), %% <<"configuredAudienceModelArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_audience_generation_jobs_request() :: #{binary() => any()}. %% Example: %% list_collaboration_configured_model_algorithm_associations_response() :: #{ %% <<"collaborationConfiguredModelAlgorithmAssociations">> => list(collaboration_configured_model_algorithm_association_summary()), %% <<"nextToken">> => string() %% } -type list_collaboration_configured_model_algorithm_associations_response() :: #{binary() => any()}. %% Example: %% get_configured_model_algorithm_response() :: #{ %% <<"configuredModelAlgorithmArn">> => string(), %% <<"createTime">> => [non_neg_integer()], %% <<"description">> => string(), %% <<"inferenceContainerConfig">> => inference_container_config(), %% <<"kmsKeyArn">> => string(), %% <<"name">> => string(), %% <<"roleArn">> => string(), %% <<"tags">> => map(), %% <<"trainingContainerConfig">> => container_config(), %% <<"updateTime">> => [non_neg_integer()] %% } -type get_configured_model_algorithm_response() :: #{binary() => any()}. %% Example: %% trained_model_export_output_configuration() :: #{ %% <<"members">> => list(trained_model_export_receiver_member()) %% } -type trained_model_export_output_configuration() :: #{binary() => any()}. %% Example: %% list_audience_generation_jobs_response() :: #{ %% <<"audienceGenerationJobs">> => list(audience_generation_job_summary()), %% <<"nextToken">> => string() %% } -type list_audience_generation_jobs_response() :: #{binary() => any()}. %% Example: %% list_audience_models_response() :: #{ %% <<"audienceModels">> => list(audience_model_summary()), %% <<"nextToken">> => string() %% } -type list_audience_models_response() :: #{binary() => any()}. %% Example: %% list_audience_export_jobs_request() :: #{ %% <<"audienceGenerationJobArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_audience_export_jobs_request() :: #{binary() => any()}. %% Example: %% delete_configured_audience_model_policy_request() :: #{} -type delete_configured_audience_model_policy_request() :: #{}. %% Example: %% get_collaboration_ml_input_channel_request() :: #{} -type get_collaboration_ml_input_channel_request() :: #{}. %% Example: %% list_trained_models_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_trained_models_request() :: #{binary() => any()}. %% Example: %% get_configured_audience_model_policy_request() :: #{} -type get_configured_audience_model_policy_request() :: #{}. %% Example: %% update_configured_audience_model_request() :: #{ %% <<"audienceModelArn">> => string(), %% <<"audienceSizeConfig">> => audience_size_config(), %% <<"description">> => string(), %% <<"minMatchingSeedSize">> => integer(), %% <<"outputConfig">> => configured_audience_model_output_config(), %% <<"sharedAudienceMetrics">> => list(list(any())()) %% } -type update_configured_audience_model_request() :: #{binary() => any()}. %% Example: %% delete_trained_model_output_request() :: #{ %% <<"versionIdentifier">> => string() %% } -type delete_trained_model_output_request() :: #{binary() => any()}. %% Example: %% get_audience_generation_job_request() :: #{} -type get_audience_generation_job_request() :: #{}. %% Example: %% list_collaboration_trained_model_inference_jobs_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"trainedModelArn">> => string(), %% <<"trainedModelVersionIdentifier">> => string() %% } -type list_collaboration_trained_model_inference_jobs_request() :: #{binary() => any()}. %% Example: %% synthetic_data_configuration() :: #{ %% <<"syntheticDataEvaluationScores">> => synthetic_data_evaluation_scores(), %% <<"syntheticDataParameters">> => ml_synthetic_data_parameters() %% } -type synthetic_data_configuration() :: #{binary() => any()}. -type cancel_trained_model_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type cancel_trained_model_inference_job_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type create_audience_model_errors() :: validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | resource_not_found_exception() | conflict_exception(). -type create_configured_audience_model_errors() :: validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | resource_not_found_exception() | conflict_exception(). -type create_configured_model_algorithm_errors() :: validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | conflict_exception(). -type create_configured_model_algorithm_association_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | resource_not_found_exception() | conflict_exception(). -type create_ml_input_channel_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | resource_not_found_exception() | conflict_exception(). -type create_trained_model_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | resource_not_found_exception() | conflict_exception() | internal_service_exception(). -type create_training_dataset_errors() :: validation_exception() | access_denied_exception() | conflict_exception(). -type delete_audience_generation_job_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type delete_audience_model_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type delete_configured_audience_model_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type delete_configured_audience_model_policy_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type delete_configured_model_algorithm_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type delete_configured_model_algorithm_association_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type delete_ml_configuration_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type delete_ml_input_channel_data_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type delete_trained_model_output_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type delete_training_dataset_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type get_audience_generation_job_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_audience_model_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_collaboration_configured_model_algorithm_association_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_collaboration_ml_input_channel_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_collaboration_trained_model_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_configured_audience_model_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_configured_audience_model_policy_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_configured_model_algorithm_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_configured_model_algorithm_association_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_ml_configuration_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_ml_input_channel_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_trained_model_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_trained_model_inference_job_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type get_training_dataset_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type list_audience_export_jobs_errors() :: validation_exception() | access_denied_exception(). -type list_audience_generation_jobs_errors() :: validation_exception() | access_denied_exception(). -type list_audience_models_errors() :: validation_exception() | access_denied_exception(). -type list_collaboration_configured_model_algorithm_associations_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_collaboration_ml_input_channels_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_collaboration_trained_model_export_jobs_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_collaboration_trained_model_inference_jobs_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_collaboration_trained_models_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_configured_audience_models_errors() :: validation_exception() | access_denied_exception(). -type list_configured_model_algorithm_associations_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_configured_model_algorithms_errors() :: validation_exception() | access_denied_exception(). -type list_ml_input_channels_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_tags_for_resource_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type list_trained_model_inference_jobs_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_trained_model_versions_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception(). -type list_trained_models_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type list_training_datasets_errors() :: validation_exception() | access_denied_exception(). -type put_configured_audience_model_policy_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type put_ml_configuration_errors() :: throttling_exception() | validation_exception() | access_denied_exception(). -type start_audience_export_job_errors() :: validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | resource_not_found_exception() | conflict_exception(). -type start_audience_generation_job_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | resource_not_found_exception() | conflict_exception(). -type start_trained_model_export_job_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). -type start_trained_model_inference_job_errors() :: throttling_exception() | validation_exception() | access_denied_exception() | service_quota_exceeded_exception() | resource_not_found_exception() | conflict_exception(). -type tag_resource_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type untag_resource_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception(). -type update_configured_audience_model_errors() :: validation_exception() | access_denied_exception() | resource_not_found_exception() | conflict_exception(). %%==================================================================== %% API %%==================================================================== %% @doc Submits a request to cancel the trained model job. -spec cancel_trained_model(aws_client:aws_client(), binary() | list(), binary() | list(), cancel_trained_model_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, cancel_trained_model_errors(), tuple()}. cancel_trained_model(Client, MembershipIdentifier, TrainedModelArn, Input) -> cancel_trained_model(Client, MembershipIdentifier, TrainedModelArn, Input, []). -spec cancel_trained_model(aws_client:aws_client(), binary() | list(), binary() | list(), cancel_trained_model_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, cancel_trained_model_errors(), tuple()}. cancel_trained_model(Client, MembershipIdentifier, TrainedModelArn, Input0, Options0) -> Method = patch, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-models/", aws_util:encode_uri(TrainedModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, QueryMapping = [ {<<"versionIdentifier">>, <<"versionIdentifier">>} ], {Query_, Input} = aws_request:build_headers(QueryMapping, Input2), request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Submits a request to cancel a trained model inference job. -spec cancel_trained_model_inference_job(aws_client:aws_client(), binary() | list(), binary() | list(), cancel_trained_model_inference_job_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, cancel_trained_model_inference_job_errors(), tuple()}. cancel_trained_model_inference_job(Client, MembershipIdentifier, TrainedModelInferenceJobArn, Input) -> cancel_trained_model_inference_job(Client, MembershipIdentifier, TrainedModelInferenceJobArn, Input, []). -spec cancel_trained_model_inference_job(aws_client:aws_client(), binary() | list(), binary() | list(), cancel_trained_model_inference_job_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, cancel_trained_model_inference_job_errors(), tuple()}. cancel_trained_model_inference_job(Client, MembershipIdentifier, TrainedModelInferenceJobArn, Input0, Options0) -> Method = patch, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-model-inference-jobs/", aws_util:encode_uri(TrainedModelInferenceJobArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Defines the information necessary to create an audience model. %% %% An audience model is a machine learning model that Clean Rooms ML trains %% to measure similarity between users. Clean Rooms ML manages training and %% storing the audience model. The audience model can be used in multiple %% calls to the `StartAudienceGenerationJob' API. -spec create_audience_model(aws_client:aws_client(), create_audience_model_request()) -> {ok, create_audience_model_response(), tuple()} | {error, any()} | {error, create_audience_model_errors(), tuple()}. create_audience_model(Client, Input) -> create_audience_model(Client, Input, []). -spec create_audience_model(aws_client:aws_client(), create_audience_model_request(), proplists:proplist()) -> {ok, create_audience_model_response(), tuple()} | {error, any()} | {error, create_audience_model_errors(), tuple()}. create_audience_model(Client, Input0, Options0) -> Method = post, Path = ["/audience-model"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Defines the information necessary to create a configured audience %% model. -spec create_configured_audience_model(aws_client:aws_client(), create_configured_audience_model_request()) -> {ok, create_configured_audience_model_response(), tuple()} | {error, any()} | {error, create_configured_audience_model_errors(), tuple()}. create_configured_audience_model(Client, Input) -> create_configured_audience_model(Client, Input, []). -spec create_configured_audience_model(aws_client:aws_client(), create_configured_audience_model_request(), proplists:proplist()) -> {ok, create_configured_audience_model_response(), tuple()} | {error, any()} | {error, create_configured_audience_model_errors(), tuple()}. create_configured_audience_model(Client, Input0, Options0) -> Method = post, Path = ["/configured-audience-model"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Creates a configured model algorithm using a container image stored %% in an ECR repository. -spec create_configured_model_algorithm(aws_client:aws_client(), create_configured_model_algorithm_request()) -> {ok, create_configured_model_algorithm_response(), tuple()} | {error, any()} | {error, create_configured_model_algorithm_errors(), tuple()}. create_configured_model_algorithm(Client, Input) -> create_configured_model_algorithm(Client, Input, []). -spec create_configured_model_algorithm(aws_client:aws_client(), create_configured_model_algorithm_request(), proplists:proplist()) -> {ok, create_configured_model_algorithm_response(), tuple()} | {error, any()} | {error, create_configured_model_algorithm_errors(), tuple()}. create_configured_model_algorithm(Client, Input0, Options0) -> Method = post, Path = ["/configured-model-algorithms"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Associates a configured model algorithm to a collaboration for use by %% any member of the collaboration. -spec create_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), create_configured_model_algorithm_association_request()) -> {ok, create_configured_model_algorithm_association_response(), tuple()} | {error, any()} | {error, create_configured_model_algorithm_association_errors(), tuple()}. create_configured_model_algorithm_association(Client, MembershipIdentifier, Input) -> create_configured_model_algorithm_association(Client, MembershipIdentifier, Input, []). -spec create_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), create_configured_model_algorithm_association_request(), proplists:proplist()) -> {ok, create_configured_model_algorithm_association_response(), tuple()} | {error, any()} | {error, create_configured_model_algorithm_association_errors(), tuple()}. create_configured_model_algorithm_association(Client, MembershipIdentifier, Input0, Options0) -> Method = post, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/configured-model-algorithm-associations"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Provides the information to create an ML input channel. %% %% An ML input channel is the result of a query that can be used for ML %% modeling. -spec create_ml_input_channel(aws_client:aws_client(), binary() | list(), create_ml_input_channel_request()) -> {ok, create_ml_input_channel_response(), tuple()} | {error, any()} | {error, create_ml_input_channel_errors(), tuple()}. create_ml_input_channel(Client, MembershipIdentifier, Input) -> create_ml_input_channel(Client, MembershipIdentifier, Input, []). -spec create_ml_input_channel(aws_client:aws_client(), binary() | list(), create_ml_input_channel_request(), proplists:proplist()) -> {ok, create_ml_input_channel_response(), tuple()} | {error, any()} | {error, create_ml_input_channel_errors(), tuple()}. create_ml_input_channel(Client, MembershipIdentifier, Input0, Options0) -> Method = post, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/ml-input-channels"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Creates a trained model from an associated configured model algorithm %% using data from any member of the collaboration. -spec create_trained_model(aws_client:aws_client(), binary() | list(), create_trained_model_request()) -> {ok, create_trained_model_response(), tuple()} | {error, any()} | {error, create_trained_model_errors(), tuple()}. create_trained_model(Client, MembershipIdentifier, Input) -> create_trained_model(Client, MembershipIdentifier, Input, []). -spec create_trained_model(aws_client:aws_client(), binary() | list(), create_trained_model_request(), proplists:proplist()) -> {ok, create_trained_model_response(), tuple()} | {error, any()} | {error, create_trained_model_errors(), tuple()}. create_trained_model(Client, MembershipIdentifier, Input0, Options0) -> Method = post, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-models"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Defines the information necessary to create a training dataset. %% %% In Clean Rooms ML, the `TrainingDataset' is metadata that points to a %% Glue table, which is read only during `AudienceModel' creation. -spec create_training_dataset(aws_client:aws_client(), create_training_dataset_request()) -> {ok, create_training_dataset_response(), tuple()} | {error, any()} | {error, create_training_dataset_errors(), tuple()}. create_training_dataset(Client, Input) -> create_training_dataset(Client, Input, []). -spec create_training_dataset(aws_client:aws_client(), create_training_dataset_request(), proplists:proplist()) -> {ok, create_training_dataset_response(), tuple()} | {error, any()} | {error, create_training_dataset_errors(), tuple()}. create_training_dataset(Client, Input0, Options0) -> Method = post, Path = ["/training-dataset"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the specified audience generation job, and removes all data %% associated with the job. -spec delete_audience_generation_job(aws_client:aws_client(), binary() | list(), delete_audience_generation_job_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_audience_generation_job_errors(), tuple()}. delete_audience_generation_job(Client, AudienceGenerationJobArn, Input) -> delete_audience_generation_job(Client, AudienceGenerationJobArn, Input, []). -spec delete_audience_generation_job(aws_client:aws_client(), binary() | list(), delete_audience_generation_job_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_audience_generation_job_errors(), tuple()}. delete_audience_generation_job(Client, AudienceGenerationJobArn, Input0, Options0) -> Method = delete, Path = ["/audience-generation-job/", aws_util:encode_uri(AudienceGenerationJobArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Specifies an audience model that you want to delete. %% %% You can't delete an audience model if there are any configured %% audience models that depend on the audience model. -spec delete_audience_model(aws_client:aws_client(), binary() | list(), delete_audience_model_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_audience_model_errors(), tuple()}. delete_audience_model(Client, AudienceModelArn, Input) -> delete_audience_model(Client, AudienceModelArn, Input, []). -spec delete_audience_model(aws_client:aws_client(), binary() | list(), delete_audience_model_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_audience_model_errors(), tuple()}. delete_audience_model(Client, AudienceModelArn, Input0, Options0) -> Method = delete, Path = ["/audience-model/", aws_util:encode_uri(AudienceModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the specified configured audience model. %% %% You can't delete a configured audience model if there are any %% lookalike models that use the configured audience model. If you delete a %% configured audience model, it will be removed from any collaborations that %% it is associated to. -spec delete_configured_audience_model(aws_client:aws_client(), binary() | list(), delete_configured_audience_model_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_configured_audience_model_errors(), tuple()}. delete_configured_audience_model(Client, ConfiguredAudienceModelArn, Input) -> delete_configured_audience_model(Client, ConfiguredAudienceModelArn, Input, []). -spec delete_configured_audience_model(aws_client:aws_client(), binary() | list(), delete_configured_audience_model_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_configured_audience_model_errors(), tuple()}. delete_configured_audience_model(Client, ConfiguredAudienceModelArn, Input0, Options0) -> Method = delete, Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the specified configured audience model policy. -spec delete_configured_audience_model_policy(aws_client:aws_client(), binary() | list(), delete_configured_audience_model_policy_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_configured_audience_model_policy_errors(), tuple()}. delete_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input) -> delete_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input, []). -spec delete_configured_audience_model_policy(aws_client:aws_client(), binary() | list(), delete_configured_audience_model_policy_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_configured_audience_model_policy_errors(), tuple()}. delete_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input0, Options0) -> Method = delete, Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), "/policy"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes a configured model algorithm. -spec delete_configured_model_algorithm(aws_client:aws_client(), binary() | list(), delete_configured_model_algorithm_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_configured_model_algorithm_errors(), tuple()}. delete_configured_model_algorithm(Client, ConfiguredModelAlgorithmArn, Input) -> delete_configured_model_algorithm(Client, ConfiguredModelAlgorithmArn, Input, []). -spec delete_configured_model_algorithm(aws_client:aws_client(), binary() | list(), delete_configured_model_algorithm_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_configured_model_algorithm_errors(), tuple()}. delete_configured_model_algorithm(Client, ConfiguredModelAlgorithmArn, Input0, Options0) -> Method = delete, Path = ["/configured-model-algorithms/", aws_util:encode_uri(ConfiguredModelAlgorithmArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes a configured model algorithm association. -spec delete_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), binary() | list(), delete_configured_model_algorithm_association_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_configured_model_algorithm_association_errors(), tuple()}. delete_configured_model_algorithm_association(Client, ConfiguredModelAlgorithmAssociationArn, MembershipIdentifier, Input) -> delete_configured_model_algorithm_association(Client, ConfiguredModelAlgorithmAssociationArn, MembershipIdentifier, Input, []). -spec delete_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), binary() | list(), delete_configured_model_algorithm_association_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_configured_model_algorithm_association_errors(), tuple()}. delete_configured_model_algorithm_association(Client, ConfiguredModelAlgorithmAssociationArn, MembershipIdentifier, Input0, Options0) -> Method = delete, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/configured-model-algorithm-associations/", aws_util:encode_uri(ConfiguredModelAlgorithmAssociationArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes a ML modeling configuration. -spec delete_ml_configuration(aws_client:aws_client(), binary() | list(), delete_ml_configuration_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_ml_configuration_errors(), tuple()}. delete_ml_configuration(Client, MembershipIdentifier, Input) -> delete_ml_configuration(Client, MembershipIdentifier, Input, []). -spec delete_ml_configuration(aws_client:aws_client(), binary() | list(), delete_ml_configuration_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_ml_configuration_errors(), tuple()}. delete_ml_configuration(Client, MembershipIdentifier, Input0, Options0) -> Method = delete, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/ml-configurations"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Provides the information necessary to delete an ML input channel. -spec delete_ml_input_channel_data(aws_client:aws_client(), binary() | list(), binary() | list(), delete_ml_input_channel_data_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_ml_input_channel_data_errors(), tuple()}. delete_ml_input_channel_data(Client, MembershipIdentifier, MlInputChannelArn, Input) -> delete_ml_input_channel_data(Client, MembershipIdentifier, MlInputChannelArn, Input, []). -spec delete_ml_input_channel_data(aws_client:aws_client(), binary() | list(), binary() | list(), delete_ml_input_channel_data_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_ml_input_channel_data_errors(), tuple()}. delete_ml_input_channel_data(Client, MembershipIdentifier, MlInputChannelArn, Input0, Options0) -> Method = delete, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/ml-input-channels/", aws_util:encode_uri(MlInputChannelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the model artifacts stored by the service. -spec delete_trained_model_output(aws_client:aws_client(), binary() | list(), binary() | list(), delete_trained_model_output_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_trained_model_output_errors(), tuple()}. delete_trained_model_output(Client, MembershipIdentifier, TrainedModelArn, Input) -> delete_trained_model_output(Client, MembershipIdentifier, TrainedModelArn, Input, []). -spec delete_trained_model_output(aws_client:aws_client(), binary() | list(), binary() | list(), delete_trained_model_output_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_trained_model_output_errors(), tuple()}. delete_trained_model_output(Client, MembershipIdentifier, TrainedModelArn, Input0, Options0) -> Method = delete, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-models/", aws_util:encode_uri(TrainedModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, QueryMapping = [ {<<"versionIdentifier">>, <<"versionIdentifier">>} ], {Query_, Input} = aws_request:build_headers(QueryMapping, Input2), request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Specifies a training dataset that you want to delete. %% %% You can't delete a training dataset if there are any audience models %% that depend on the training dataset. In Clean Rooms ML, the %% `TrainingDataset' is metadata that points to a Glue table, which is %% read only during `AudienceModel' creation. This action deletes the %% metadata. -spec delete_training_dataset(aws_client:aws_client(), binary() | list(), delete_training_dataset_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_training_dataset_errors(), tuple()}. delete_training_dataset(Client, TrainingDatasetArn, Input) -> delete_training_dataset(Client, TrainingDatasetArn, Input, []). -spec delete_training_dataset(aws_client:aws_client(), binary() | list(), delete_training_dataset_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_training_dataset_errors(), tuple()}. delete_training_dataset(Client, TrainingDatasetArn, Input0, Options0) -> Method = delete, Path = ["/training-dataset/", aws_util:encode_uri(TrainingDatasetArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Returns information about an audience generation job. -spec get_audience_generation_job(aws_client:aws_client(), binary() | list()) -> {ok, get_audience_generation_job_response(), tuple()} | {error, any()} | {error, get_audience_generation_job_errors(), tuple()}. get_audience_generation_job(Client, AudienceGenerationJobArn) when is_map(Client) -> get_audience_generation_job(Client, AudienceGenerationJobArn, #{}, #{}). -spec get_audience_generation_job(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, get_audience_generation_job_response(), tuple()} | {error, any()} | {error, get_audience_generation_job_errors(), tuple()}. get_audience_generation_job(Client, AudienceGenerationJobArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_audience_generation_job(Client, AudienceGenerationJobArn, QueryMap, HeadersMap, []). -spec get_audience_generation_job(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_audience_generation_job_response(), tuple()} | {error, any()} | {error, get_audience_generation_job_errors(), tuple()}. get_audience_generation_job(Client, AudienceGenerationJobArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-generation-job/", aws_util:encode_uri(AudienceGenerationJobArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about an audience model -spec get_audience_model(aws_client:aws_client(), binary() | list()) -> {ok, get_audience_model_response(), tuple()} | {error, any()} | {error, get_audience_model_errors(), tuple()}. get_audience_model(Client, AudienceModelArn) when is_map(Client) -> get_audience_model(Client, AudienceModelArn, #{}, #{}). -spec get_audience_model(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, get_audience_model_response(), tuple()} | {error, any()} | {error, get_audience_model_errors(), tuple()}. get_audience_model(Client, AudienceModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_audience_model(Client, AudienceModelArn, QueryMap, HeadersMap, []). -spec get_audience_model(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_audience_model_response(), tuple()} | {error, any()} | {error, get_audience_model_errors(), tuple()}. get_audience_model(Client, AudienceModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-model/", aws_util:encode_uri(AudienceModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about the configured model algorithm association %% in a collaboration. -spec get_collaboration_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, get_collaboration_configured_model_algorithm_association_response(), tuple()} | {error, any()} | {error, get_collaboration_configured_model_algorithm_association_errors(), tuple()}. get_collaboration_configured_model_algorithm_association(Client, CollaborationIdentifier, ConfiguredModelAlgorithmAssociationArn) when is_map(Client) -> get_collaboration_configured_model_algorithm_association(Client, CollaborationIdentifier, ConfiguredModelAlgorithmAssociationArn, #{}, #{}). -spec get_collaboration_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, get_collaboration_configured_model_algorithm_association_response(), tuple()} | {error, any()} | {error, get_collaboration_configured_model_algorithm_association_errors(), tuple()}. get_collaboration_configured_model_algorithm_association(Client, CollaborationIdentifier, ConfiguredModelAlgorithmAssociationArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_collaboration_configured_model_algorithm_association(Client, CollaborationIdentifier, ConfiguredModelAlgorithmAssociationArn, QueryMap, HeadersMap, []). -spec get_collaboration_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_collaboration_configured_model_algorithm_association_response(), tuple()} | {error, any()} | {error, get_collaboration_configured_model_algorithm_association_errors(), tuple()}. get_collaboration_configured_model_algorithm_association(Client, CollaborationIdentifier, ConfiguredModelAlgorithmAssociationArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/collaborations/", aws_util:encode_uri(CollaborationIdentifier), "/configured-model-algorithm-associations/", aws_util:encode_uri(ConfiguredModelAlgorithmAssociationArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a specific ML input channel in a %% collaboration. -spec get_collaboration_ml_input_channel(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, get_collaboration_ml_input_channel_response(), tuple()} | {error, any()} | {error, get_collaboration_ml_input_channel_errors(), tuple()}. get_collaboration_ml_input_channel(Client, CollaborationIdentifier, MlInputChannelArn) when is_map(Client) -> get_collaboration_ml_input_channel(Client, CollaborationIdentifier, MlInputChannelArn, #{}, #{}). -spec get_collaboration_ml_input_channel(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, get_collaboration_ml_input_channel_response(), tuple()} | {error, any()} | {error, get_collaboration_ml_input_channel_errors(), tuple()}. get_collaboration_ml_input_channel(Client, CollaborationIdentifier, MlInputChannelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_collaboration_ml_input_channel(Client, CollaborationIdentifier, MlInputChannelArn, QueryMap, HeadersMap, []). -spec get_collaboration_ml_input_channel(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_collaboration_ml_input_channel_response(), tuple()} | {error, any()} | {error, get_collaboration_ml_input_channel_errors(), tuple()}. get_collaboration_ml_input_channel(Client, CollaborationIdentifier, MlInputChannelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/collaborations/", aws_util:encode_uri(CollaborationIdentifier), "/ml-input-channels/", aws_util:encode_uri(MlInputChannelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a trained model in a collaboration. -spec get_collaboration_trained_model(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, get_collaboration_trained_model_response(), tuple()} | {error, any()} | {error, get_collaboration_trained_model_errors(), tuple()}. get_collaboration_trained_model(Client, CollaborationIdentifier, TrainedModelArn) when is_map(Client) -> get_collaboration_trained_model(Client, CollaborationIdentifier, TrainedModelArn, #{}, #{}). -spec get_collaboration_trained_model(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, get_collaboration_trained_model_response(), tuple()} | {error, any()} | {error, get_collaboration_trained_model_errors(), tuple()}. get_collaboration_trained_model(Client, CollaborationIdentifier, TrainedModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_collaboration_trained_model(Client, CollaborationIdentifier, TrainedModelArn, QueryMap, HeadersMap, []). -spec get_collaboration_trained_model(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_collaboration_trained_model_response(), tuple()} | {error, any()} | {error, get_collaboration_trained_model_errors(), tuple()}. get_collaboration_trained_model(Client, CollaborationIdentifier, TrainedModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/collaborations/", aws_util:encode_uri(CollaborationIdentifier), "/trained-models/", aws_util:encode_uri(TrainedModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"versionIdentifier">>, maps:get(<<"versionIdentifier">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a specified configured audience model. -spec get_configured_audience_model(aws_client:aws_client(), binary() | list()) -> {ok, get_configured_audience_model_response(), tuple()} | {error, any()} | {error, get_configured_audience_model_errors(), tuple()}. get_configured_audience_model(Client, ConfiguredAudienceModelArn) when is_map(Client) -> get_configured_audience_model(Client, ConfiguredAudienceModelArn, #{}, #{}). -spec get_configured_audience_model(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, get_configured_audience_model_response(), tuple()} | {error, any()} | {error, get_configured_audience_model_errors(), tuple()}. get_configured_audience_model(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_configured_audience_model(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap, []). -spec get_configured_audience_model(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_configured_audience_model_response(), tuple()} | {error, any()} | {error, get_configured_audience_model_errors(), tuple()}. get_configured_audience_model(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a configured audience model policy. -spec get_configured_audience_model_policy(aws_client:aws_client(), binary() | list()) -> {ok, get_configured_audience_model_policy_response(), tuple()} | {error, any()} | {error, get_configured_audience_model_policy_errors(), tuple()}. get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn) when is_map(Client) -> get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, #{}, #{}). -spec get_configured_audience_model_policy(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, get_configured_audience_model_policy_response(), tuple()} | {error, any()} | {error, get_configured_audience_model_policy_errors(), tuple()}. get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap, []). -spec get_configured_audience_model_policy(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_configured_audience_model_policy_response(), tuple()} | {error, any()} | {error, get_configured_audience_model_policy_errors(), tuple()}. get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), "/policy"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a configured model algorithm. -spec get_configured_model_algorithm(aws_client:aws_client(), binary() | list()) -> {ok, get_configured_model_algorithm_response(), tuple()} | {error, any()} | {error, get_configured_model_algorithm_errors(), tuple()}. get_configured_model_algorithm(Client, ConfiguredModelAlgorithmArn) when is_map(Client) -> get_configured_model_algorithm(Client, ConfiguredModelAlgorithmArn, #{}, #{}). -spec get_configured_model_algorithm(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, get_configured_model_algorithm_response(), tuple()} | {error, any()} | {error, get_configured_model_algorithm_errors(), tuple()}. get_configured_model_algorithm(Client, ConfiguredModelAlgorithmArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_configured_model_algorithm(Client, ConfiguredModelAlgorithmArn, QueryMap, HeadersMap, []). -spec get_configured_model_algorithm(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_configured_model_algorithm_response(), tuple()} | {error, any()} | {error, get_configured_model_algorithm_errors(), tuple()}. get_configured_model_algorithm(Client, ConfiguredModelAlgorithmArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/configured-model-algorithms/", aws_util:encode_uri(ConfiguredModelAlgorithmArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a configured model algorithm association. -spec get_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, get_configured_model_algorithm_association_response(), tuple()} | {error, any()} | {error, get_configured_model_algorithm_association_errors(), tuple()}. get_configured_model_algorithm_association(Client, ConfiguredModelAlgorithmAssociationArn, MembershipIdentifier) when is_map(Client) -> get_configured_model_algorithm_association(Client, ConfiguredModelAlgorithmAssociationArn, MembershipIdentifier, #{}, #{}). -spec get_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, get_configured_model_algorithm_association_response(), tuple()} | {error, any()} | {error, get_configured_model_algorithm_association_errors(), tuple()}. get_configured_model_algorithm_association(Client, ConfiguredModelAlgorithmAssociationArn, MembershipIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_configured_model_algorithm_association(Client, ConfiguredModelAlgorithmAssociationArn, MembershipIdentifier, QueryMap, HeadersMap, []). -spec get_configured_model_algorithm_association(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_configured_model_algorithm_association_response(), tuple()} | {error, any()} | {error, get_configured_model_algorithm_association_errors(), tuple()}. get_configured_model_algorithm_association(Client, ConfiguredModelAlgorithmAssociationArn, MembershipIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/configured-model-algorithm-associations/", aws_util:encode_uri(ConfiguredModelAlgorithmAssociationArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a specific ML configuration. -spec get_ml_configuration(aws_client:aws_client(), binary() | list()) -> {ok, get_ml_configuration_response(), tuple()} | {error, any()} | {error, get_ml_configuration_errors(), tuple()}. get_ml_configuration(Client, MembershipIdentifier) when is_map(Client) -> get_ml_configuration(Client, MembershipIdentifier, #{}, #{}). -spec get_ml_configuration(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, get_ml_configuration_response(), tuple()} | {error, any()} | {error, get_ml_configuration_errors(), tuple()}. get_ml_configuration(Client, MembershipIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_ml_configuration(Client, MembershipIdentifier, QueryMap, HeadersMap, []). -spec get_ml_configuration(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_ml_configuration_response(), tuple()} | {error, any()} | {error, get_ml_configuration_errors(), tuple()}. get_ml_configuration(Client, MembershipIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/ml-configurations"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about an ML input channel. -spec get_ml_input_channel(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, get_ml_input_channel_response(), tuple()} | {error, any()} | {error, get_ml_input_channel_errors(), tuple()}. get_ml_input_channel(Client, MembershipIdentifier, MlInputChannelArn) when is_map(Client) -> get_ml_input_channel(Client, MembershipIdentifier, MlInputChannelArn, #{}, #{}). -spec get_ml_input_channel(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, get_ml_input_channel_response(), tuple()} | {error, any()} | {error, get_ml_input_channel_errors(), tuple()}. get_ml_input_channel(Client, MembershipIdentifier, MlInputChannelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_ml_input_channel(Client, MembershipIdentifier, MlInputChannelArn, QueryMap, HeadersMap, []). -spec get_ml_input_channel(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_ml_input_channel_response(), tuple()} | {error, any()} | {error, get_ml_input_channel_errors(), tuple()}. get_ml_input_channel(Client, MembershipIdentifier, MlInputChannelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/ml-input-channels/", aws_util:encode_uri(MlInputChannelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a trained model. -spec get_trained_model(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, get_trained_model_response(), tuple()} | {error, any()} | {error, get_trained_model_errors(), tuple()}. get_trained_model(Client, MembershipIdentifier, TrainedModelArn) when is_map(Client) -> get_trained_model(Client, MembershipIdentifier, TrainedModelArn, #{}, #{}). -spec get_trained_model(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, get_trained_model_response(), tuple()} | {error, any()} | {error, get_trained_model_errors(), tuple()}. get_trained_model(Client, MembershipIdentifier, TrainedModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_trained_model(Client, MembershipIdentifier, TrainedModelArn, QueryMap, HeadersMap, []). -spec get_trained_model(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_trained_model_response(), tuple()} | {error, any()} | {error, get_trained_model_errors(), tuple()}. get_trained_model(Client, MembershipIdentifier, TrainedModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-models/", aws_util:encode_uri(TrainedModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"versionIdentifier">>, maps:get(<<"versionIdentifier">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a trained model inference job. -spec get_trained_model_inference_job(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, get_trained_model_inference_job_response(), tuple()} | {error, any()} | {error, get_trained_model_inference_job_errors(), tuple()}. get_trained_model_inference_job(Client, MembershipIdentifier, TrainedModelInferenceJobArn) when is_map(Client) -> get_trained_model_inference_job(Client, MembershipIdentifier, TrainedModelInferenceJobArn, #{}, #{}). -spec get_trained_model_inference_job(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, get_trained_model_inference_job_response(), tuple()} | {error, any()} | {error, get_trained_model_inference_job_errors(), tuple()}. get_trained_model_inference_job(Client, MembershipIdentifier, TrainedModelInferenceJobArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_trained_model_inference_job(Client, MembershipIdentifier, TrainedModelInferenceJobArn, QueryMap, HeadersMap, []). -spec get_trained_model_inference_job(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_trained_model_inference_job_response(), tuple()} | {error, any()} | {error, get_trained_model_inference_job_errors(), tuple()}. get_trained_model_inference_job(Client, MembershipIdentifier, TrainedModelInferenceJobArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-model-inference-jobs/", aws_util:encode_uri(TrainedModelInferenceJobArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a training dataset. -spec get_training_dataset(aws_client:aws_client(), binary() | list()) -> {ok, get_training_dataset_response(), tuple()} | {error, any()} | {error, get_training_dataset_errors(), tuple()}. get_training_dataset(Client, TrainingDatasetArn) when is_map(Client) -> get_training_dataset(Client, TrainingDatasetArn, #{}, #{}). -spec get_training_dataset(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, get_training_dataset_response(), tuple()} | {error, any()} | {error, get_training_dataset_errors(), tuple()}. get_training_dataset(Client, TrainingDatasetArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_training_dataset(Client, TrainingDatasetArn, QueryMap, HeadersMap, []). -spec get_training_dataset(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, get_training_dataset_response(), tuple()} | {error, any()} | {error, get_training_dataset_errors(), tuple()}. get_training_dataset(Client, TrainingDatasetArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/training-dataset/", aws_util:encode_uri(TrainingDatasetArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of the audience export jobs. -spec list_audience_export_jobs(aws_client:aws_client()) -> {ok, list_audience_export_jobs_response(), tuple()} | {error, any()} | {error, list_audience_export_jobs_errors(), tuple()}. list_audience_export_jobs(Client) when is_map(Client) -> list_audience_export_jobs(Client, #{}, #{}). -spec list_audience_export_jobs(aws_client:aws_client(), map(), map()) -> {ok, list_audience_export_jobs_response(), tuple()} | {error, any()} | {error, list_audience_export_jobs_errors(), tuple()}. list_audience_export_jobs(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_audience_export_jobs(Client, QueryMap, HeadersMap, []). -spec list_audience_export_jobs(aws_client:aws_client(), map(), map(), proplists:proplist()) -> {ok, list_audience_export_jobs_response(), tuple()} | {error, any()} | {error, list_audience_export_jobs_errors(), tuple()}. list_audience_export_jobs(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-export-job"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"audienceGenerationJobArn">>, maps:get(<<"audienceGenerationJobArn">>, QueryMap, undefined)}, {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of audience generation jobs. -spec list_audience_generation_jobs(aws_client:aws_client()) -> {ok, list_audience_generation_jobs_response(), tuple()} | {error, any()} | {error, list_audience_generation_jobs_errors(), tuple()}. list_audience_generation_jobs(Client) when is_map(Client) -> list_audience_generation_jobs(Client, #{}, #{}). -spec list_audience_generation_jobs(aws_client:aws_client(), map(), map()) -> {ok, list_audience_generation_jobs_response(), tuple()} | {error, any()} | {error, list_audience_generation_jobs_errors(), tuple()}. list_audience_generation_jobs(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_audience_generation_jobs(Client, QueryMap, HeadersMap, []). -spec list_audience_generation_jobs(aws_client:aws_client(), map(), map(), proplists:proplist()) -> {ok, list_audience_generation_jobs_response(), tuple()} | {error, any()} | {error, list_audience_generation_jobs_errors(), tuple()}. list_audience_generation_jobs(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-generation-job"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"collaborationId">>, maps:get(<<"collaborationId">>, QueryMap, undefined)}, {<<"configuredAudienceModelArn">>, maps:get(<<"configuredAudienceModelArn">>, QueryMap, undefined)}, {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of audience models. -spec list_audience_models(aws_client:aws_client()) -> {ok, list_audience_models_response(), tuple()} | {error, any()} | {error, list_audience_models_errors(), tuple()}. list_audience_models(Client) when is_map(Client) -> list_audience_models(Client, #{}, #{}). -spec list_audience_models(aws_client:aws_client(), map(), map()) -> {ok, list_audience_models_response(), tuple()} | {error, any()} | {error, list_audience_models_errors(), tuple()}. list_audience_models(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_audience_models(Client, QueryMap, HeadersMap, []). -spec list_audience_models(aws_client:aws_client(), map(), map(), proplists:proplist()) -> {ok, list_audience_models_response(), tuple()} | {error, any()} | {error, list_audience_models_errors(), tuple()}. list_audience_models(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-model"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of the configured model algorithm associations in a %% collaboration. -spec list_collaboration_configured_model_algorithm_associations(aws_client:aws_client(), binary() | list()) -> {ok, list_collaboration_configured_model_algorithm_associations_response(), tuple()} | {error, any()} | {error, list_collaboration_configured_model_algorithm_associations_errors(), tuple()}. list_collaboration_configured_model_algorithm_associations(Client, CollaborationIdentifier) when is_map(Client) -> list_collaboration_configured_model_algorithm_associations(Client, CollaborationIdentifier, #{}, #{}). -spec list_collaboration_configured_model_algorithm_associations(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_collaboration_configured_model_algorithm_associations_response(), tuple()} | {error, any()} | {error, list_collaboration_configured_model_algorithm_associations_errors(), tuple()}. list_collaboration_configured_model_algorithm_associations(Client, CollaborationIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_collaboration_configured_model_algorithm_associations(Client, CollaborationIdentifier, QueryMap, HeadersMap, []). -spec list_collaboration_configured_model_algorithm_associations(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_collaboration_configured_model_algorithm_associations_response(), tuple()} | {error, any()} | {error, list_collaboration_configured_model_algorithm_associations_errors(), tuple()}. list_collaboration_configured_model_algorithm_associations(Client, CollaborationIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/collaborations/", aws_util:encode_uri(CollaborationIdentifier), "/configured-model-algorithm-associations"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of the ML input channels in a collaboration. -spec list_collaboration_ml_input_channels(aws_client:aws_client(), binary() | list()) -> {ok, list_collaboration_ml_input_channels_response(), tuple()} | {error, any()} | {error, list_collaboration_ml_input_channels_errors(), tuple()}. list_collaboration_ml_input_channels(Client, CollaborationIdentifier) when is_map(Client) -> list_collaboration_ml_input_channels(Client, CollaborationIdentifier, #{}, #{}). -spec list_collaboration_ml_input_channels(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_collaboration_ml_input_channels_response(), tuple()} | {error, any()} | {error, list_collaboration_ml_input_channels_errors(), tuple()}. list_collaboration_ml_input_channels(Client, CollaborationIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_collaboration_ml_input_channels(Client, CollaborationIdentifier, QueryMap, HeadersMap, []). -spec list_collaboration_ml_input_channels(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_collaboration_ml_input_channels_response(), tuple()} | {error, any()} | {error, list_collaboration_ml_input_channels_errors(), tuple()}. list_collaboration_ml_input_channels(Client, CollaborationIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/collaborations/", aws_util:encode_uri(CollaborationIdentifier), "/ml-input-channels"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of the export jobs for a trained model in a %% collaboration. -spec list_collaboration_trained_model_export_jobs(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, list_collaboration_trained_model_export_jobs_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_model_export_jobs_errors(), tuple()}. list_collaboration_trained_model_export_jobs(Client, CollaborationIdentifier, TrainedModelArn) when is_map(Client) -> list_collaboration_trained_model_export_jobs(Client, CollaborationIdentifier, TrainedModelArn, #{}, #{}). -spec list_collaboration_trained_model_export_jobs(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, list_collaboration_trained_model_export_jobs_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_model_export_jobs_errors(), tuple()}. list_collaboration_trained_model_export_jobs(Client, CollaborationIdentifier, TrainedModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_collaboration_trained_model_export_jobs(Client, CollaborationIdentifier, TrainedModelArn, QueryMap, HeadersMap, []). -spec list_collaboration_trained_model_export_jobs(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_collaboration_trained_model_export_jobs_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_model_export_jobs_errors(), tuple()}. list_collaboration_trained_model_export_jobs(Client, CollaborationIdentifier, TrainedModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/collaborations/", aws_util:encode_uri(CollaborationIdentifier), "/trained-models/", aws_util:encode_uri(TrainedModelArn), "/export-jobs"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)}, {<<"trainedModelVersionIdentifier">>, maps:get(<<"trainedModelVersionIdentifier">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of trained model inference jobs in a specified %% collaboration. -spec list_collaboration_trained_model_inference_jobs(aws_client:aws_client(), binary() | list()) -> {ok, list_collaboration_trained_model_inference_jobs_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_model_inference_jobs_errors(), tuple()}. list_collaboration_trained_model_inference_jobs(Client, CollaborationIdentifier) when is_map(Client) -> list_collaboration_trained_model_inference_jobs(Client, CollaborationIdentifier, #{}, #{}). -spec list_collaboration_trained_model_inference_jobs(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_collaboration_trained_model_inference_jobs_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_model_inference_jobs_errors(), tuple()}. list_collaboration_trained_model_inference_jobs(Client, CollaborationIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_collaboration_trained_model_inference_jobs(Client, CollaborationIdentifier, QueryMap, HeadersMap, []). -spec list_collaboration_trained_model_inference_jobs(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_collaboration_trained_model_inference_jobs_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_model_inference_jobs_errors(), tuple()}. list_collaboration_trained_model_inference_jobs(Client, CollaborationIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/collaborations/", aws_util:encode_uri(CollaborationIdentifier), "/trained-model-inference-jobs"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)}, {<<"trainedModelArn">>, maps:get(<<"trainedModelArn">>, QueryMap, undefined)}, {<<"trainedModelVersionIdentifier">>, maps:get(<<"trainedModelVersionIdentifier">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of the trained models in a collaboration. -spec list_collaboration_trained_models(aws_client:aws_client(), binary() | list()) -> {ok, list_collaboration_trained_models_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_models_errors(), tuple()}. list_collaboration_trained_models(Client, CollaborationIdentifier) when is_map(Client) -> list_collaboration_trained_models(Client, CollaborationIdentifier, #{}, #{}). -spec list_collaboration_trained_models(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_collaboration_trained_models_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_models_errors(), tuple()}. list_collaboration_trained_models(Client, CollaborationIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_collaboration_trained_models(Client, CollaborationIdentifier, QueryMap, HeadersMap, []). -spec list_collaboration_trained_models(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_collaboration_trained_models_response(), tuple()} | {error, any()} | {error, list_collaboration_trained_models_errors(), tuple()}. list_collaboration_trained_models(Client, CollaborationIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/collaborations/", aws_util:encode_uri(CollaborationIdentifier), "/trained-models"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of the configured audience models. -spec list_configured_audience_models(aws_client:aws_client()) -> {ok, list_configured_audience_models_response(), tuple()} | {error, any()} | {error, list_configured_audience_models_errors(), tuple()}. list_configured_audience_models(Client) when is_map(Client) -> list_configured_audience_models(Client, #{}, #{}). -spec list_configured_audience_models(aws_client:aws_client(), map(), map()) -> {ok, list_configured_audience_models_response(), tuple()} | {error, any()} | {error, list_configured_audience_models_errors(), tuple()}. list_configured_audience_models(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_configured_audience_models(Client, QueryMap, HeadersMap, []). -spec list_configured_audience_models(aws_client:aws_client(), map(), map(), proplists:proplist()) -> {ok, list_configured_audience_models_response(), tuple()} | {error, any()} | {error, list_configured_audience_models_errors(), tuple()}. list_configured_audience_models(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/configured-audience-model"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of configured model algorithm associations. -spec list_configured_model_algorithm_associations(aws_client:aws_client(), binary() | list()) -> {ok, list_configured_model_algorithm_associations_response(), tuple()} | {error, any()} | {error, list_configured_model_algorithm_associations_errors(), tuple()}. list_configured_model_algorithm_associations(Client, MembershipIdentifier) when is_map(Client) -> list_configured_model_algorithm_associations(Client, MembershipIdentifier, #{}, #{}). -spec list_configured_model_algorithm_associations(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_configured_model_algorithm_associations_response(), tuple()} | {error, any()} | {error, list_configured_model_algorithm_associations_errors(), tuple()}. list_configured_model_algorithm_associations(Client, MembershipIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_configured_model_algorithm_associations(Client, MembershipIdentifier, QueryMap, HeadersMap, []). -spec list_configured_model_algorithm_associations(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_configured_model_algorithm_associations_response(), tuple()} | {error, any()} | {error, list_configured_model_algorithm_associations_errors(), tuple()}. list_configured_model_algorithm_associations(Client, MembershipIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/configured-model-algorithm-associations"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of configured model algorithms. -spec list_configured_model_algorithms(aws_client:aws_client()) -> {ok, list_configured_model_algorithms_response(), tuple()} | {error, any()} | {error, list_configured_model_algorithms_errors(), tuple()}. list_configured_model_algorithms(Client) when is_map(Client) -> list_configured_model_algorithms(Client, #{}, #{}). -spec list_configured_model_algorithms(aws_client:aws_client(), map(), map()) -> {ok, list_configured_model_algorithms_response(), tuple()} | {error, any()} | {error, list_configured_model_algorithms_errors(), tuple()}. list_configured_model_algorithms(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_configured_model_algorithms(Client, QueryMap, HeadersMap, []). -spec list_configured_model_algorithms(aws_client:aws_client(), map(), map(), proplists:proplist()) -> {ok, list_configured_model_algorithms_response(), tuple()} | {error, any()} | {error, list_configured_model_algorithms_errors(), tuple()}. list_configured_model_algorithms(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/configured-model-algorithms"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of ML input channels. -spec list_ml_input_channels(aws_client:aws_client(), binary() | list()) -> {ok, list_ml_input_channels_response(), tuple()} | {error, any()} | {error, list_ml_input_channels_errors(), tuple()}. list_ml_input_channels(Client, MembershipIdentifier) when is_map(Client) -> list_ml_input_channels(Client, MembershipIdentifier, #{}, #{}). -spec list_ml_input_channels(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_ml_input_channels_response(), tuple()} | {error, any()} | {error, list_ml_input_channels_errors(), tuple()}. list_ml_input_channels(Client, MembershipIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_ml_input_channels(Client, MembershipIdentifier, QueryMap, HeadersMap, []). -spec list_ml_input_channels(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_ml_input_channels_response(), tuple()} | {error, any()} | {error, list_ml_input_channels_errors(), tuple()}. list_ml_input_channels(Client, MembershipIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/ml-input-channels"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of tags for a provided resource. -spec list_tags_for_resource(aws_client:aws_client(), binary() | list()) -> {ok, list_tags_for_resource_response(), tuple()} | {error, any()} | {error, list_tags_for_resource_errors(), tuple()}. list_tags_for_resource(Client, ResourceArn) when is_map(Client) -> list_tags_for_resource(Client, ResourceArn, #{}, #{}). -spec list_tags_for_resource(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_tags_for_resource_response(), tuple()} | {error, any()} | {error, list_tags_for_resource_errors(), tuple()}. list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap, []). -spec list_tags_for_resource(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_tags_for_resource_response(), tuple()} | {error, any()} | {error, list_tags_for_resource_errors(), tuple()}. list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of trained model inference jobs that match the request %% parameters. -spec list_trained_model_inference_jobs(aws_client:aws_client(), binary() | list()) -> {ok, list_trained_model_inference_jobs_response(), tuple()} | {error, any()} | {error, list_trained_model_inference_jobs_errors(), tuple()}. list_trained_model_inference_jobs(Client, MembershipIdentifier) when is_map(Client) -> list_trained_model_inference_jobs(Client, MembershipIdentifier, #{}, #{}). -spec list_trained_model_inference_jobs(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_trained_model_inference_jobs_response(), tuple()} | {error, any()} | {error, list_trained_model_inference_jobs_errors(), tuple()}. list_trained_model_inference_jobs(Client, MembershipIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_trained_model_inference_jobs(Client, MembershipIdentifier, QueryMap, HeadersMap, []). -spec list_trained_model_inference_jobs(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_trained_model_inference_jobs_response(), tuple()} | {error, any()} | {error, list_trained_model_inference_jobs_errors(), tuple()}. list_trained_model_inference_jobs(Client, MembershipIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-model-inference-jobs"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)}, {<<"trainedModelArn">>, maps:get(<<"trainedModelArn">>, QueryMap, undefined)}, {<<"trainedModelVersionIdentifier">>, maps:get(<<"trainedModelVersionIdentifier">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of trained model versions for a specified trained %% model. %% %% This operation allows you to view all versions of a trained model, %% including information about their status and creation details. You can use %% this to track the evolution of your trained models and select specific %% versions for inference or further training. -spec list_trained_model_versions(aws_client:aws_client(), binary() | list(), binary() | list()) -> {ok, list_trained_model_versions_response(), tuple()} | {error, any()} | {error, list_trained_model_versions_errors(), tuple()}. list_trained_model_versions(Client, MembershipIdentifier, TrainedModelArn) when is_map(Client) -> list_trained_model_versions(Client, MembershipIdentifier, TrainedModelArn, #{}, #{}). -spec list_trained_model_versions(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map()) -> {ok, list_trained_model_versions_response(), tuple()} | {error, any()} | {error, list_trained_model_versions_errors(), tuple()}. list_trained_model_versions(Client, MembershipIdentifier, TrainedModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_trained_model_versions(Client, MembershipIdentifier, TrainedModelArn, QueryMap, HeadersMap, []). -spec list_trained_model_versions(aws_client:aws_client(), binary() | list(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_trained_model_versions_response(), tuple()} | {error, any()} | {error, list_trained_model_versions_errors(), tuple()}. list_trained_model_versions(Client, MembershipIdentifier, TrainedModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-models/", aws_util:encode_uri(TrainedModelArn), "/versions"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)}, {<<"status">>, maps:get(<<"status">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of trained models. -spec list_trained_models(aws_client:aws_client(), binary() | list()) -> {ok, list_trained_models_response(), tuple()} | {error, any()} | {error, list_trained_models_errors(), tuple()}. list_trained_models(Client, MembershipIdentifier) when is_map(Client) -> list_trained_models(Client, MembershipIdentifier, #{}, #{}). -spec list_trained_models(aws_client:aws_client(), binary() | list(), map(), map()) -> {ok, list_trained_models_response(), tuple()} | {error, any()} | {error, list_trained_models_errors(), tuple()}. list_trained_models(Client, MembershipIdentifier, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_trained_models(Client, MembershipIdentifier, QueryMap, HeadersMap, []). -spec list_trained_models(aws_client:aws_client(), binary() | list(), map(), map(), proplists:proplist()) -> {ok, list_trained_models_response(), tuple()} | {error, any()} | {error, list_trained_models_errors(), tuple()}. list_trained_models(Client, MembershipIdentifier, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-models"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of training datasets. -spec list_training_datasets(aws_client:aws_client()) -> {ok, list_training_datasets_response(), tuple()} | {error, any()} | {error, list_training_datasets_errors(), tuple()}. list_training_datasets(Client) when is_map(Client) -> list_training_datasets(Client, #{}, #{}). -spec list_training_datasets(aws_client:aws_client(), map(), map()) -> {ok, list_training_datasets_response(), tuple()} | {error, any()} | {error, list_training_datasets_errors(), tuple()}. list_training_datasets(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_training_datasets(Client, QueryMap, HeadersMap, []). -spec list_training_datasets(aws_client:aws_client(), map(), map(), proplists:proplist()) -> {ok, list_training_datasets_response(), tuple()} | {error, any()} | {error, list_training_datasets_errors(), tuple()}. list_training_datasets(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/training-dataset"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary} | Options2], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Create or update the resource policy for a configured audience model. -spec put_configured_audience_model_policy(aws_client:aws_client(), binary() | list(), put_configured_audience_model_policy_request()) -> {ok, put_configured_audience_model_policy_response(), tuple()} | {error, any()} | {error, put_configured_audience_model_policy_errors(), tuple()}. put_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input) -> put_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input, []). -spec put_configured_audience_model_policy(aws_client:aws_client(), binary() | list(), put_configured_audience_model_policy_request(), proplists:proplist()) -> {ok, put_configured_audience_model_policy_response(), tuple()} | {error, any()} | {error, put_configured_audience_model_policy_errors(), tuple()}. put_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input0, Options0) -> Method = put, Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), "/policy"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Assigns information about an ML configuration. -spec put_ml_configuration(aws_client:aws_client(), binary() | list(), put_ml_configuration_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, put_ml_configuration_errors(), tuple()}. put_ml_configuration(Client, MembershipIdentifier, Input) -> put_ml_configuration(Client, MembershipIdentifier, Input, []). -spec put_ml_configuration(aws_client:aws_client(), binary() | list(), put_ml_configuration_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, put_ml_configuration_errors(), tuple()}. put_ml_configuration(Client, MembershipIdentifier, Input0, Options0) -> Method = put, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/ml-configurations"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Export an audience of a specified size after you have generated an %% audience. -spec start_audience_export_job(aws_client:aws_client(), start_audience_export_job_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, start_audience_export_job_errors(), tuple()}. start_audience_export_job(Client, Input) -> start_audience_export_job(Client, Input, []). -spec start_audience_export_job(aws_client:aws_client(), start_audience_export_job_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, start_audience_export_job_errors(), tuple()}. start_audience_export_job(Client, Input0, Options0) -> Method = post, Path = ["/audience-export-job"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Information necessary to start the audience generation job. -spec start_audience_generation_job(aws_client:aws_client(), start_audience_generation_job_request()) -> {ok, start_audience_generation_job_response(), tuple()} | {error, any()} | {error, start_audience_generation_job_errors(), tuple()}. start_audience_generation_job(Client, Input) -> start_audience_generation_job(Client, Input, []). -spec start_audience_generation_job(aws_client:aws_client(), start_audience_generation_job_request(), proplists:proplist()) -> {ok, start_audience_generation_job_response(), tuple()} | {error, any()} | {error, start_audience_generation_job_errors(), tuple()}. start_audience_generation_job(Client, Input0, Options0) -> Method = post, Path = ["/audience-generation-job"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Provides the information necessary to start a trained model export %% job. -spec start_trained_model_export_job(aws_client:aws_client(), binary() | list(), binary() | list(), start_trained_model_export_job_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, start_trained_model_export_job_errors(), tuple()}. start_trained_model_export_job(Client, MembershipIdentifier, TrainedModelArn, Input) -> start_trained_model_export_job(Client, MembershipIdentifier, TrainedModelArn, Input, []). -spec start_trained_model_export_job(aws_client:aws_client(), binary() | list(), binary() | list(), start_trained_model_export_job_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, start_trained_model_export_job_errors(), tuple()}. start_trained_model_export_job(Client, MembershipIdentifier, TrainedModelArn, Input0, Options0) -> Method = post, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-models/", aws_util:encode_uri(TrainedModelArn), "/export-jobs"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Defines the information necessary to begin a trained model inference %% job. -spec start_trained_model_inference_job(aws_client:aws_client(), binary() | list(), start_trained_model_inference_job_request()) -> {ok, start_trained_model_inference_job_response(), tuple()} | {error, any()} | {error, start_trained_model_inference_job_errors(), tuple()}. start_trained_model_inference_job(Client, MembershipIdentifier, Input) -> start_trained_model_inference_job(Client, MembershipIdentifier, Input, []). -spec start_trained_model_inference_job(aws_client:aws_client(), binary() | list(), start_trained_model_inference_job_request(), proplists:proplist()) -> {ok, start_trained_model_inference_job_response(), tuple()} | {error, any()} | {error, start_trained_model_inference_job_errors(), tuple()}. start_trained_model_inference_job(Client, MembershipIdentifier, Input0, Options0) -> Method = post, Path = ["/memberships/", aws_util:encode_uri(MembershipIdentifier), "/trained-model-inference-jobs"], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Adds metadata tags to a specified resource. -spec tag_resource(aws_client:aws_client(), binary() | list(), tag_resource_request()) -> {ok, tag_resource_response(), tuple()} | {error, any()} | {error, tag_resource_errors(), tuple()}. tag_resource(Client, ResourceArn, Input) -> tag_resource(Client, ResourceArn, Input, []). -spec tag_resource(aws_client:aws_client(), binary() | list(), tag_resource_request(), proplists:proplist()) -> {ok, tag_resource_response(), tuple()} | {error, any()} | {error, tag_resource_errors(), tuple()}. tag_resource(Client, ResourceArn, Input0, Options0) -> Method = post, Path = ["/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Removes metadata tags from a specified resource. -spec untag_resource(aws_client:aws_client(), binary() | list(), untag_resource_request()) -> {ok, untag_resource_response(), tuple()} | {error, any()} | {error, untag_resource_errors(), tuple()}. untag_resource(Client, ResourceArn, Input) -> untag_resource(Client, ResourceArn, Input, []). -spec untag_resource(aws_client:aws_client(), binary() | list(), untag_resource_request(), proplists:proplist()) -> {ok, untag_resource_response(), tuple()} | {error, any()} | {error, untag_resource_errors(), tuple()}. untag_resource(Client, ResourceArn, Input0, Options0) -> Method = delete, Path = ["/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, QueryMapping = [ {<<"tagKeys">>, <<"tagKeys">>} ], {Query_, Input} = aws_request:build_headers(QueryMapping, Input2), request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Provides the information necessary to update a configured audience %% model. %% %% Updates that impact audience generation jobs take effect when a new job %% starts, but do not impact currently running jobs. -spec update_configured_audience_model(aws_client:aws_client(), binary() | list(), update_configured_audience_model_request()) -> {ok, update_configured_audience_model_response(), tuple()} | {error, any()} | {error, update_configured_audience_model_errors(), tuple()}. update_configured_audience_model(Client, ConfiguredAudienceModelArn, Input) -> update_configured_audience_model(Client, ConfiguredAudienceModelArn, Input, []). -spec update_configured_audience_model(aws_client:aws_client(), binary() | list(), update_configured_audience_model_request(), proplists:proplist()) -> {ok, update_configured_audience_model_response(), tuple()} | {error, any()} | {error, update_configured_audience_model_errors(), tuple()}. update_configured_audience_model(Client, ConfiguredAudienceModelArn, Input0, Options0) -> Method = patch, Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), ""], SuccessStatusCode = 200, {SendBodyAsBinary, Options1} = proplists_take(send_body_as_binary, Options0, false), {ReceiveBodyAsBinary, Options2} = proplists_take(receive_body_as_binary, Options1, false), Options = [{send_body_as_binary, SendBodyAsBinary}, {receive_body_as_binary, ReceiveBodyAsBinary}, {append_sha256_content_hash, false} | Options2], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %%==================================================================== %% Internal functions %%==================================================================== -spec proplists_take(any(), proplists:proplist(), any()) -> {any(), proplists:proplist()}. proplists_take(Key, Proplist, Default) -> Value = proplists:get_value(Key, Proplist, Default), {Value, proplists:delete(Key, Proplist)}. -spec request(aws_client:aws_client(), atom(), iolist(), list(), list(), map() | undefined, list(), pos_integer() | undefined) -> {ok, {integer(), list()}} | {ok, Result, {integer(), list(), hackney:client()}} | {error, Error, {integer(), list(), hackney:client()}} | {error, term()} when Result :: map(), Error :: map(). request(Client, Method, Path, Query, Headers0, Input, Options, SuccessStatusCode) -> RequestFun = fun() -> do_request(Client, Method, Path, Query, Headers0, Input, Options, SuccessStatusCode) end, aws_request:request(RequestFun, Options). do_request(Client, Method, Path, Query, Headers0, Input, Options, SuccessStatusCode) -> Client1 = Client#{service => <<"cleanrooms-ml">>}, Host = build_host(<<"cleanrooms-ml">>, Client1), URL0 = build_url(Host, Path, Client1), URL = aws_request:add_query(URL0, Query), AdditionalHeaders1 = [ {<<"Host">>, Host} , {<<"Content-Type">>, <<"application/x-amz-json-1.1">>} ], Payload = case proplists:get_value(send_body_as_binary, Options) of true -> maps:get(<<"Body">>, Input, <<"">>); false -> encode_payload(Input) end, AdditionalHeaders = case proplists:get_value(append_sha256_content_hash, Options, false) of true -> add_checksum_hash_header(AdditionalHeaders1, Payload); false -> AdditionalHeaders1 end, Headers1 = aws_request:add_headers(AdditionalHeaders, Headers0), MethodBin = aws_request:method_to_binary(Method), SignedHeaders = aws_request:sign_request(Client1, MethodBin, URL, Headers1, Payload), Response = hackney:request(Method, URL, SignedHeaders, Payload, Options), DecodeBody = not proplists:get_value(receive_body_as_binary, Options), handle_response(Response, SuccessStatusCode, DecodeBody). add_checksum_hash_header(Headers, Body) -> [ {<<"X-Amz-CheckSum-SHA256">>, base64:encode(crypto:hash(sha256, Body))} | Headers ]. handle_response({ok, StatusCode, ResponseHeaders}, SuccessStatusCode, _DecodeBody) when StatusCode =:= 200; StatusCode =:= 202; StatusCode =:= 204; StatusCode =:= 206; StatusCode =:= SuccessStatusCode -> {ok, {StatusCode, ResponseHeaders}}; handle_response({ok, StatusCode, ResponseHeaders}, _, _DecodeBody) -> {error, {StatusCode, ResponseHeaders}}; handle_response({ok, StatusCode, ResponseHeaders, Client}, SuccessStatusCode, DecodeBody) when StatusCode =:= 200; StatusCode =:= 202; StatusCode =:= 204; StatusCode =:= 206; StatusCode =:= SuccessStatusCode -> case hackney:body(Client) of {ok, <<>>} when StatusCode =:= 200; StatusCode =:= SuccessStatusCode -> {ok, #{}, {StatusCode, ResponseHeaders, Client}}; {ok, Body} -> Result = case DecodeBody of true -> try jsx:decode(Body) catch Error:Reason:Stack -> erlang:raise(error, {body_decode_failed, Error, Reason, StatusCode, Body}, Stack) end; false -> #{<<"Body">> => Body} end, {ok, Result, {StatusCode, ResponseHeaders, Client}} end; handle_response({ok, StatusCode, _ResponseHeaders, _Client}, _, _DecodeBody) when StatusCode =:= 503 -> %% Retriable error if retries are enabled {error, service_unavailable}; handle_response({ok, StatusCode, ResponseHeaders, Client}, _, _DecodeBody) -> {ok, Body} = hackney:body(Client), try DecodedError = jsx:decode(Body), {error, DecodedError, {StatusCode, ResponseHeaders, Client}} catch Error:Reason:Stack -> erlang:raise(error, {body_decode_failed, Error, Reason, StatusCode, Body}, Stack) end; handle_response({error, Reason}, _, _DecodeBody) -> {error, Reason}. build_host(_EndpointPrefix, #{region := <<"local">>, endpoint := Endpoint}) -> Endpoint; build_host(_EndpointPrefix, #{region := <<"local">>}) -> <<"localhost">>; build_host(EndpointPrefix, #{region := Region, endpoint := Endpoint}) -> aws_util:binary_join([EndpointPrefix, Region, Endpoint], <<".">>). build_url(Host, Path0, Client) -> Proto = aws_client:proto(Client), Path = erlang:iolist_to_binary(Path0), Port = aws_client:port(Client), aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, Path], <<"">>). -spec encode_payload(undefined | map()) -> binary(). encode_payload(undefined) -> <<>>; encode_payload(Input) -> jsx:encode(Input).