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              "--location",
              "{{$.inputs.parameters['location']}}",
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              "CustomJob",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--payload",
              "{\"display_name\": \"automl-tabular-transform-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}\", \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:prod\", \"args\": [\"transform\", \"--transform_output_artifact_path={{$.outputs.artifacts['transform_output'].uri}}\", \"--transform_output_path={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/transform\", \"--materialized_splits_output_path={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/transform_materialized\", \"--metadata_path={{$.inputs.artifacts['metadata'].uri}}\", \"--dataset_schema_path={{$.inputs.artifacts['dataset_schema'].uri}}\", \"--train_split={{$.inputs.artifacts['train_split'].uri}}\", \"--eval_split={{$.inputs.artifacts['eval_split'].uri}}\", \"--test_split={{$.inputs.artifacts['test_split'].uri}}\", \"--materialized_train_split={{$.outputs.artifacts['materialized_train_split'].uri}}\", \"--materialized_eval_split={{$.outputs.artifacts['materialized_eval_split'].uri}}\", \"--materialized_test_split={{$.outputs.artifacts['materialized_test_split'].uri}}\", \"--training_schema_path={{$.outputs.artifacts['training_schema_uri'].uri}}\", \"--job_name=automl-tabular-transform-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}\", \"--dataflow_project={{$.inputs.parameters['project']}}\", \"--error_file_path={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--dataflow_staging_dir={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_staging\", \"--dataflow_tmp_dir={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers={{$.inputs.parameters['dataflow_max_num_workers']}}\", \"--dataflow_machine_type={{$.inputs.parameters['dataflow_machine_type']}}\", \"--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:prod\", \"--dataflow_disk_size_gb={{$.inputs.parameters['dataflow_disk_size_gb']}}\", \"--dataflow_subnetwork_fully_qualified={{$.inputs.parameters['dataflow_subnetwork']}}\", \"--dataflow_use_public_ips={{$.inputs.parameters['dataflow_use_public_ips']}}\", \"--dataflow_kms_key={{$.inputs.parameters['encryption_spec_key_name']}}\", \"--dataflow_service_account={{$.inputs.parameters['dataflow_service_account']}}\"]}}]}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.v1.gcp_launcher.launcher"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.10"
          }
        },
        "exec-bool-identity": {
          "container": {
            "args": [
              "--value",
              "{{$.inputs.parameters['value']}}",
              "----output-paths",
              "{{$.outputs.parameters['Output'].output_file}}"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
              "def _bool_identity(value):\n    \"\"\"Returns boolean value.\n\n    Args:\n      value: Boolean value to return\n\n    Returns:\n      Boolean value.\n    \"\"\"\n    return \"true\" if value else \"false\"\n\ndef _deserialize_bool(s) -> bool:\n    from distutils.util import strtobool\n    return strtobool(s) == 1\n\ndef _serialize_str(str_value: str) -> str:\n    if not isinstance(str_value, str):\n        raise TypeError('Value \"{}\" has type \"{}\" instead of str.'.format(\n            str(str_value), str(type(str_value))))\n    return str_value\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Bool identity', description='Returns boolean value.')\n_parser.add_argument(\"--value\", dest=\"value\", type=_deserialize_bool, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = _bool_identity(**_parsed_args)\n\n_outputs = [_outputs]\n\n_output_serializers = [\n    _serialize_str,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n    try:\n        os.makedirs(os.path.dirname(output_file))\n    except OSError:\n        pass\n    with open(output_file, 'w') as f:\n        f.write(_output_serializers[idx](_outputs[idx]))\n"
            ],
            "image": "python:3.7-slim"
          }
        },
        "exec-bool-identity-2": {
          "container": {
            "args": [
              "--value",
              "{{$.inputs.parameters['value']}}",
              "----output-paths",
              "{{$.outputs.parameters['Output'].output_file}}"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
              "def _bool_identity(value):\n    \"\"\"Returns boolean value.\n\n    Args:\n      value: Boolean value to return\n\n    Returns:\n      Boolean value.\n    \"\"\"\n    return \"true\" if value else \"false\"\n\ndef _deserialize_bool(s) -> bool:\n    from distutils.util import strtobool\n    return strtobool(s) == 1\n\ndef _serialize_str(str_value: str) -> str:\n    if not isinstance(str_value, str):\n        raise TypeError('Value \"{}\" has type \"{}\" instead of str.'.format(\n            str(str_value), str(type(str_value))))\n    return str_value\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Bool identity', description='Returns boolean value.')\n_parser.add_argument(\"--value\", dest=\"value\", type=_deserialize_bool, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = _bool_identity(**_parsed_args)\n\n_outputs = [_outputs]\n\n_output_serializers = [\n    _serialize_str,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n    try:\n        os.makedirs(os.path.dirname(output_file))\n    except OSError:\n        pass\n    with open(output_file, 'w') as f:\n        f.write(_output_serializers[idx](_outputs[idx]))\n"
            ],
            "image": "python:3.7-slim"
          }
        },
        "exec-merge-materialized-splits": {
          "container": {
            "args": [
              "--split-0",
              "{{$.inputs.artifacts['split_0'].path}}",
              "--split-1",
              "{{$.inputs.artifacts['split_1'].path}}",
              "--splits",
              "{{$.outputs.artifacts['splits'].path}}"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
              "def _make_parent_dirs_and_return_path(file_path: str):\n    import os\n    os.makedirs(os.path.dirname(file_path), exist_ok=True)\n    return file_path\n\ndef _merge_materialized_splits(\n    split_0,\n    split_1,\n    splits,\n):\n    \"\"\"Merge two materialized splits.\n\n    Args:\n      split_0: The first materialized split.\n      split_1: The second materialized split.\n      splits: The merged materialized split.\n    \"\"\"\n    with open(split_0, \"r\") as f:\n        split_0_content = f.read()\n    with open(split_1, \"r\") as f:\n        split_1_content = f.read()\n    with open(splits, \"w\") as f:\n        f.write(\",\".join([split_0_content, split_1_content]))\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Merge materialized splits', description='Merge two materialized splits.')\n_parser.add_argument(\"--split-0\", dest=\"split_0\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--split-1\", dest=\"split_1\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--splits\", dest=\"splits\", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\n_outputs = _merge_materialized_splits(**_parsed_args)\n"
            ],
            "image": "python:3.7-slim"
          }
        },
        "exec-model-batch-explanation": {
          "container": {
            "args": [
              "--type",
              "BatchPredictionJob",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['job_display_name']}}\",  \"input_config\": {\"instances_format\": \"{{$.inputs.parameters['instances_format']}}\", \"gcs_source\": {\"uris\":{{$.inputs.parameters['gcs_source_uris']}}}, \"bigquery_source\": {\"input_uri\": \"{{$.inputs.parameters['bigquery_source_input_uri']}}\"}}, \"model_parameters\": {{$.inputs.parameters['model_parameters']}}, \"output_config\": {\"predictions_format\": \"{{$.inputs.parameters['predictions_format']}}\", \"gcs_destination\": {\"output_uri_prefix\": \"{{$.inputs.parameters['gcs_destination_output_uri_prefix']}}\"}, \"bigquery_destination\": {\"output_uri\": \"{{$.inputs.parameters['bigquery_destination_output_uri']}}\"}}, \"dedicated_resources\": {\"machine_spec\": {\"machine_type\": \"{{$.inputs.parameters['machine_type']}}\", \"accelerator_type\": \"{{$.inputs.parameters['accelerator_type']}}\", \"accelerator_count\": {{$.inputs.parameters['accelerator_count']}}}, \"starting_replica_count\": {{$.inputs.parameters['starting_replica_count']}}, \"max_replica_count\": {{$.inputs.parameters['max_replica_count']}}}, \"manual_batch_tuning_parameters\": {\"batch_size\": {{$.inputs.parameters['manual_batch_tuning_parameters_batch_size']}}}, \"generate_explanation\": {{$.inputs.parameters['generate_explanation']}}, \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"explanation_metadata_artifact\": \"{{$.inputs.artifacts['explanation_metadata_artifact'].uri}}\", \"labels\": {{$.inputs.parameters['labels']}}, \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "launcher"
            ],
            "image": "gcr.io/ml-pipeline/automl-tables-private:1.0.11"
          }
        },
        "exec-model-batch-explanation-2": {
          "container": {
            "args": [
              "--type",
              "BatchPredictionJob",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['job_display_name']}}\",  \"input_config\": {\"instances_format\": \"{{$.inputs.parameters['instances_format']}}\", \"gcs_source\": {\"uris\":{{$.inputs.parameters['gcs_source_uris']}}}, \"bigquery_source\": {\"input_uri\": \"{{$.inputs.parameters['bigquery_source_input_uri']}}\"}}, \"model_parameters\": {{$.inputs.parameters['model_parameters']}}, \"output_config\": {\"predictions_format\": \"{{$.inputs.parameters['predictions_format']}}\", \"gcs_destination\": {\"output_uri_prefix\": \"{{$.inputs.parameters['gcs_destination_output_uri_prefix']}}\"}, \"bigquery_destination\": {\"output_uri\": \"{{$.inputs.parameters['bigquery_destination_output_uri']}}\"}}, \"dedicated_resources\": {\"machine_spec\": {\"machine_type\": \"{{$.inputs.parameters['machine_type']}}\", \"accelerator_type\": \"{{$.inputs.parameters['accelerator_type']}}\", \"accelerator_count\": {{$.inputs.parameters['accelerator_count']}}}, \"starting_replica_count\": {{$.inputs.parameters['starting_replica_count']}}, \"max_replica_count\": {{$.inputs.parameters['max_replica_count']}}}, \"manual_batch_tuning_parameters\": {\"batch_size\": {{$.inputs.parameters['manual_batch_tuning_parameters_batch_size']}}}, \"generate_explanation\": {{$.inputs.parameters['generate_explanation']}}, \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"explanation_metadata_artifact\": \"{{$.inputs.artifacts['explanation_metadata_artifact'].uri}}\", \"labels\": {{$.inputs.parameters['labels']}}, \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "launcher"
            ],
            "image": "gcr.io/ml-pipeline/automl-tables-private:1.0.11"
          }
        },
        "exec-model-batch-predict": {
          "container": {
            "args": [
              "--type",
              "BatchPredictionJob",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['job_display_name']}}\",  \"input_config\": {\"instances_format\": \"{{$.inputs.parameters['instances_format']}}\", \"gcs_source\": {\"uris\":{{$.inputs.parameters['gcs_source_uris']}}}, \"bigquery_source\": {\"input_uri\": \"{{$.inputs.parameters['bigquery_source_input_uri']}}\"}}, \"model_parameters\": {{$.inputs.parameters['model_parameters']}}, \"output_config\": {\"predictions_format\": \"{{$.inputs.parameters['predictions_format']}}\", \"gcs_destination\": {\"output_uri_prefix\": \"{{$.inputs.parameters['gcs_destination_output_uri_prefix']}}\"}, \"bigquery_destination\": {\"output_uri\": \"{{$.inputs.parameters['bigquery_destination_output_uri']}}\"}}, \"dedicated_resources\": {\"machine_spec\": {\"machine_type\": \"{{$.inputs.parameters['machine_type']}}\", \"accelerator_type\": \"{{$.inputs.parameters['accelerator_type']}}\", \"accelerator_count\": {{$.inputs.parameters['accelerator_count']}}}, \"starting_replica_count\": {{$.inputs.parameters['starting_replica_count']}}, \"max_replica_count\": {{$.inputs.parameters['max_replica_count']}}}, \"manual_batch_tuning_parameters\": {\"batch_size\": {{$.inputs.parameters['manual_batch_tuning_parameters_batch_size']}}}, \"generate_explanation\": {{$.inputs.parameters['generate_explanation']}}, \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"labels\": {{$.inputs.parameters['labels']}}, \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.v1.gcp_launcher.launcher"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.10"
          }
        },
        "exec-model-batch-predict-2": {
          "container": {
            "args": [
              "--type",
              "BatchPredictionJob",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['job_display_name']}}\", \"model\": \"{{$.inputs.artifacts['model'].metadata['resourceName']}}\", \"input_config\": {\"instances_format\": \"{{$.inputs.parameters['instances_format']}}\", \"gcs_source\": {\"uris\":{{$.inputs.parameters['gcs_source_uris']}}}, \"bigquery_source\": {\"input_uri\": \"{{$.inputs.parameters['bigquery_source_input_uri']}}\"}}, \"model_parameters\": {{$.inputs.parameters['model_parameters']}}, \"output_config\": {\"predictions_format\": \"{{$.inputs.parameters['predictions_format']}}\", \"gcs_destination\": {\"output_uri_prefix\": \"{{$.inputs.parameters['gcs_destination_output_uri_prefix']}}\"}, \"bigquery_destination\": {\"output_uri\": \"{{$.inputs.parameters['bigquery_destination_output_uri']}}\"}}, \"dedicated_resources\": {\"machine_spec\": {\"machine_type\": \"{{$.inputs.parameters['machine_type']}}\", \"accelerator_type\": \"{{$.inputs.parameters['accelerator_type']}}\", \"accelerator_count\": {{$.inputs.parameters['accelerator_count']}}}, \"starting_replica_count\": {{$.inputs.parameters['starting_replica_count']}}, \"max_replica_count\": {{$.inputs.parameters['max_replica_count']}}}, \"manual_batch_tuning_parameters\": {\"batch_size\": {{$.inputs.parameters['manual_batch_tuning_parameters_batch_size']}}}, \"generate_explanation\": {{$.inputs.parameters['generate_explanation']}}, \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"labels\": {{$.inputs.parameters['labels']}}, \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.v1.gcp_launcher.launcher"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.10"
          }
        },
        "exec-model-batch-predict-3": {
          "container": {
            "args": [
              "--type",
              "BatchPredictionJob",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['job_display_name']}}\", \"model\": \"{{$.inputs.artifacts['model'].metadata['resourceName']}}\", \"input_config\": {\"instances_format\": \"{{$.inputs.parameters['instances_format']}}\", \"gcs_source\": {\"uris\":{{$.inputs.parameters['gcs_source_uris']}}}, \"bigquery_source\": {\"input_uri\": \"{{$.inputs.parameters['bigquery_source_input_uri']}}\"}}, \"model_parameters\": {{$.inputs.parameters['model_parameters']}}, \"output_config\": {\"predictions_format\": \"{{$.inputs.parameters['predictions_format']}}\", \"gcs_destination\": {\"output_uri_prefix\": \"{{$.inputs.parameters['gcs_destination_output_uri_prefix']}}\"}, \"bigquery_destination\": {\"output_uri\": \"{{$.inputs.parameters['bigquery_destination_output_uri']}}\"}}, \"dedicated_resources\": {\"machine_spec\": {\"machine_type\": \"{{$.inputs.parameters['machine_type']}}\", \"accelerator_type\": \"{{$.inputs.parameters['accelerator_type']}}\", \"accelerator_count\": {{$.inputs.parameters['accelerator_count']}}}, \"starting_replica_count\": {{$.inputs.parameters['starting_replica_count']}}, \"max_replica_count\": {{$.inputs.parameters['max_replica_count']}}}, \"manual_batch_tuning_parameters\": {\"batch_size\": {{$.inputs.parameters['manual_batch_tuning_parameters_batch_size']}}}, \"generate_explanation\": {{$.inputs.parameters['generate_explanation']}}, \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"labels\": {{$.inputs.parameters['labels']}}, \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.v1.gcp_launcher.launcher"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.10"
          }
        },
        "exec-model-batch-predict-4": {
          "container": {
            "args": [
              "--type",
              "BatchPredictionJob",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['job_display_name']}}\",  \"input_config\": {\"instances_format\": \"{{$.inputs.parameters['instances_format']}}\", \"gcs_source\": {\"uris\":{{$.inputs.parameters['gcs_source_uris']}}}, \"bigquery_source\": {\"input_uri\": \"{{$.inputs.parameters['bigquery_source_input_uri']}}\"}}, \"model_parameters\": {{$.inputs.parameters['model_parameters']}}, \"output_config\": {\"predictions_format\": \"{{$.inputs.parameters['predictions_format']}}\", \"gcs_destination\": {\"output_uri_prefix\": \"{{$.inputs.parameters['gcs_destination_output_uri_prefix']}}\"}, \"bigquery_destination\": {\"output_uri\": \"{{$.inputs.parameters['bigquery_destination_output_uri']}}\"}}, \"dedicated_resources\": {\"machine_spec\": {\"machine_type\": \"{{$.inputs.parameters['machine_type']}}\", \"accelerator_type\": \"{{$.inputs.parameters['accelerator_type']}}\", \"accelerator_count\": {{$.inputs.parameters['accelerator_count']}}}, \"starting_replica_count\": {{$.inputs.parameters['starting_replica_count']}}, \"max_replica_count\": {{$.inputs.parameters['max_replica_count']}}}, \"manual_batch_tuning_parameters\": {\"batch_size\": {{$.inputs.parameters['manual_batch_tuning_parameters_batch_size']}}}, \"generate_explanation\": {{$.inputs.parameters['generate_explanation']}}, \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"labels\": {{$.inputs.parameters['labels']}}, \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.v1.gcp_launcher.launcher"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.10"
          }
        },
        "exec-model-evaluation": {
          "container": {
            "args": [
              "--setup_file",
              "/setup.py",
              "--json_mode",
              "true",
              "--project_id",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--problem_type",
              "{{$.inputs.parameters['problem_type']}}",
              "--batch_prediction_format",
              "{{$.inputs.parameters['predictions_format']}}",
              "--batch_prediction_gcs_source",
              "{{$.inputs.artifacts['batch_prediction_job'].metadata['gcsOutputDirectory']}}",
              "--ground_truth_format",
              "{{$.inputs.parameters['ground_truth_format']}}",
              "--ground_truth_gcs_source",
              "{{$.inputs.parameters['ground_truth_gcs_source']}}",
              "--key_prefix_in_prediction_dataset",
              "instance",
              "--key_columns",
              "{{$.inputs.parameters['key_columns']}}",
              "--root_dir",
              "{{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
              "--classification_type",
              "{{$.inputs.parameters['classification_type']}}",
              "--class_names",
              "{{$.inputs.parameters['class_names']}}",
              "--ground_truth_column",
              "instance.{{$.inputs.parameters['ground_truth_column']}}",
              "--prediction_score_column",
              "{{$.inputs.parameters['prediction_score_column']}}",
              "--prediction_label_column",
              "{{$.inputs.parameters['prediction_label_column']}}",
              "--prediction_id_column",
              "{{$.inputs.parameters['prediction_id_column']}}",
              "--example_weight_column",
              "{{$.inputs.parameters['example_weight_column']}}",
              "--positive_classes",
              "{{$.inputs.parameters['positive_classes']}}",
              "--generate_feature_attribution",
              "{{$.inputs.parameters['generate_feature_attribution']}}",
              "--dataflow_job_prefix",
              "evaluation-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
              "--dataflow_service_account",
              "{{$.inputs.parameters['dataflow_service_account']}}",
              "--dataflow_disk_size",
              "{{$.inputs.parameters['dataflow_disk_size']}}",
              "--dataflow_machine_type",
              "{{$.inputs.parameters['dataflow_machine_type']}}",
              "--dataflow_workers_num",
              "{{$.inputs.parameters['dataflow_workers_num']}}",
              "--dataflow_max_workers_num",
              "{{$.inputs.parameters['dataflow_max_workers_num']}}",
              "--dataflow_subnetwork",
              "{{$.inputs.parameters['dataflow_subnetwork']}}",
              "--dataflow_use_public_ips",
              "{{$.inputs.parameters['dataflow_use_public_ips']}}",
              "--kms_key_name",
              "{{$.inputs.parameters['encryption_spec_key_name']}}",
              "--output_metrics_gcs_path",
              "{{$.outputs.artifacts['evaluation_metrics'].uri}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python",
              "/main.py"
            ],
            "image": "gcr.io/ml-pipeline/model-evaluation:v0.3"
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        "exec-model-evaluation-2": {
          "container": {
            "args": [
              "--setup_file",
              "/setup.py",
              "--json_mode",
              "true",
              "--project_id",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--problem_type",
              "{{$.inputs.parameters['problem_type']}}",
              "--batch_prediction_format",
              "{{$.inputs.parameters['predictions_format']}}",
              "--batch_prediction_gcs_source",
              "{{$.inputs.artifacts['batch_prediction_job'].metadata['gcsOutputDirectory']}}",
              "--ground_truth_format",
              "{{$.inputs.parameters['ground_truth_format']}}",
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              "{{$.inputs.parameters['ground_truth_gcs_source']}}",
              "--key_prefix_in_prediction_dataset",
              "instance",
              "--key_columns",
              "{{$.inputs.parameters['key_columns']}}",
              "--root_dir",
              "{{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
              "--classification_type",
              "{{$.inputs.parameters['classification_type']}}",
              "--class_names",
              "{{$.inputs.parameters['class_names']}}",
              "--ground_truth_column",
              "instance.{{$.inputs.parameters['ground_truth_column']}}",
              "--prediction_score_column",
              "{{$.inputs.parameters['prediction_score_column']}}",
              "--prediction_label_column",
              "{{$.inputs.parameters['prediction_label_column']}}",
              "--prediction_id_column",
              "{{$.inputs.parameters['prediction_id_column']}}",
              "--example_weight_column",
              "{{$.inputs.parameters['example_weight_column']}}",
              "--positive_classes",
              "{{$.inputs.parameters['positive_classes']}}",
              "--generate_feature_attribution",
              "{{$.inputs.parameters['generate_feature_attribution']}}",
              "--dataflow_job_prefix",
              "evaluation-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
              "--dataflow_service_account",
              "{{$.inputs.parameters['dataflow_service_account']}}",
              "--dataflow_disk_size",
              "{{$.inputs.parameters['dataflow_disk_size']}}",
              "--dataflow_machine_type",
              "{{$.inputs.parameters['dataflow_machine_type']}}",
              "--dataflow_workers_num",
              "{{$.inputs.parameters['dataflow_workers_num']}}",
              "--dataflow_max_workers_num",
              "{{$.inputs.parameters['dataflow_max_workers_num']}}",
              "--dataflow_subnetwork",
              "{{$.inputs.parameters['dataflow_subnetwork']}}",
              "--dataflow_use_public_ips",
              "{{$.inputs.parameters['dataflow_use_public_ips']}}",
              "--kms_key_name",
              "{{$.inputs.parameters['encryption_spec_key_name']}}",
              "--output_metrics_gcs_path",
              "{{$.outputs.artifacts['evaluation_metrics'].uri}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python",
              "/main.py"
            ],
            "image": "gcr.io/ml-pipeline/model-evaluation:v0.3"
          }
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        "exec-model-evaluation-3": {
          "container": {
            "args": [
              "--setup_file",
              "/setup.py",
              "--json_mode",
              "true",
              "--project_id",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--problem_type",
              "{{$.inputs.parameters['problem_type']}}",
              "--batch_prediction_format",
              "{{$.inputs.parameters['predictions_format']}}",
              "--batch_prediction_gcs_source",
              "{{$.inputs.artifacts['batch_prediction_job'].metadata['gcsOutputDirectory']}}",
              "--ground_truth_format",
              "{{$.inputs.parameters['ground_truth_format']}}",
              "--ground_truth_gcs_source",
              "{{$.inputs.parameters['ground_truth_gcs_source']}}",
              "--key_prefix_in_prediction_dataset",
              "instance",
              "--key_columns",
              "{{$.inputs.parameters['key_columns']}}",
              "--root_dir",
              "{{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
              "--classification_type",
              "{{$.inputs.parameters['classification_type']}}",
              "--class_names",
              "{{$.inputs.parameters['class_names']}}",
              "--ground_truth_column",
              "instance.{{$.inputs.parameters['ground_truth_column']}}",
              "--prediction_score_column",
              "{{$.inputs.parameters['prediction_score_column']}}",
              "--prediction_label_column",
              "{{$.inputs.parameters['prediction_label_column']}}",
              "--prediction_id_column",
              "{{$.inputs.parameters['prediction_id_column']}}",
              "--example_weight_column",
              "{{$.inputs.parameters['example_weight_column']}}",
              "--positive_classes",
              "{{$.inputs.parameters['positive_classes']}}",
              "--generate_feature_attribution",
              "{{$.inputs.parameters['generate_feature_attribution']}}",
              "--dataflow_job_prefix",
              "evaluation-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
              "--dataflow_service_account",
              "{{$.inputs.parameters['dataflow_service_account']}}",
              "--dataflow_disk_size",
              "{{$.inputs.parameters['dataflow_disk_size']}}",
              "--dataflow_machine_type",
              "{{$.inputs.parameters['dataflow_machine_type']}}",
              "--dataflow_workers_num",
              "{{$.inputs.parameters['dataflow_workers_num']}}",
              "--dataflow_max_workers_num",
              "{{$.inputs.parameters['dataflow_max_workers_num']}}",
              "--dataflow_subnetwork",
              "{{$.inputs.parameters['dataflow_subnetwork']}}",
              "--dataflow_use_public_ips",
              "{{$.inputs.parameters['dataflow_use_public_ips']}}",
              "--kms_key_name",
              "{{$.inputs.parameters['encryption_spec_key_name']}}",
              "--output_metrics_gcs_path",
              "{{$.outputs.artifacts['evaluation_metrics'].uri}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python",
              "/main.py"
            ],
            "image": "gcr.io/ml-pipeline/model-evaluation:v0.3"
          }
        },
        "exec-model-evaluation-4": {
          "container": {
            "args": [
              "--setup_file",
              "/setup.py",
              "--json_mode",
              "true",
              "--project_id",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--problem_type",
              "{{$.inputs.parameters['problem_type']}}",
              "--batch_prediction_format",
              "{{$.inputs.parameters['predictions_format']}}",
              "--batch_prediction_gcs_source",
              "{{$.inputs.artifacts['batch_prediction_job'].metadata['gcsOutputDirectory']}}",
              "--ground_truth_format",
              "{{$.inputs.parameters['ground_truth_format']}}",
              "--ground_truth_gcs_source",
              "{{$.inputs.parameters['ground_truth_gcs_source']}}",
              "--key_prefix_in_prediction_dataset",
              "instance",
              "--key_columns",
              "{{$.inputs.parameters['key_columns']}}",
              "--root_dir",
              "{{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
              "--classification_type",
              "{{$.inputs.parameters['classification_type']}}",
              "--class_names",
              "{{$.inputs.parameters['class_names']}}",
              "--ground_truth_column",
              "instance.{{$.inputs.parameters['ground_truth_column']}}",
              "--prediction_score_column",
              "{{$.inputs.parameters['prediction_score_column']}}",
              "--prediction_label_column",
              "{{$.inputs.parameters['prediction_label_column']}}",
              "--prediction_id_column",
              "{{$.inputs.parameters['prediction_id_column']}}",
              "--example_weight_column",
              "{{$.inputs.parameters['example_weight_column']}}",
              "--positive_classes",
              "{{$.inputs.parameters['positive_classes']}}",
              "--generate_feature_attribution",
              "{{$.inputs.parameters['generate_feature_attribution']}}",
              "--dataflow_job_prefix",
              "evaluation-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
              "--dataflow_service_account",
              "{{$.inputs.parameters['dataflow_service_account']}}",
              "--dataflow_disk_size",
              "{{$.inputs.parameters['dataflow_disk_size']}}",
              "--dataflow_machine_type",
              "{{$.inputs.parameters['dataflow_machine_type']}}",
              "--dataflow_workers_num",
              "{{$.inputs.parameters['dataflow_workers_num']}}",
              "--dataflow_max_workers_num",
              "{{$.inputs.parameters['dataflow_max_workers_num']}}",
              "--dataflow_subnetwork",
              "{{$.inputs.parameters['dataflow_subnetwork']}}",
              "--dataflow_use_public_ips",
              "{{$.inputs.parameters['dataflow_use_public_ips']}}",
              "--kms_key_name",
              "{{$.inputs.parameters['encryption_spec_key_name']}}",
              "--output_metrics_gcs_path",
              "{{$.outputs.artifacts['evaluation_metrics'].uri}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python",
              "/main.py"
            ],
            "image": "gcr.io/ml-pipeline/model-evaluation:v0.3"
          }
        },
        "exec-model-evaluation-import": {
          "container": {
            "args": [
              "--metrics",
              "{{$.inputs.artifacts['metrics'].path}}",
              "--metrics_explanation",
              "{{$.inputs.artifacts['metrics'].metadata['explanation_gcs_path']}}",
              "--explanation",
              "{{$.inputs.artifacts['explanation'].metadata['explanation_gcs_path']}}",
              "--problem_type",
              "{{$.inputs.parameters['problem_type']}}",
              "--display_name",
              "{{$.inputs.parameters['display_name']}}",
              "--dataset_path",
              "{{$.inputs.parameters['dataset_path']}}",
              "--dataset_type",
              "{{$.inputs.parameters['dataset_type']}}",
              "--pipeline_job_id",
              "{{$.pipeline_job_uuid}}",
              "--model_name",
              "{{$.inputs.artifacts['model'].metadata['resourceName']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.experimental.evaluation.import_model_evaluation"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.10"
          }
        },
        "exec-model-evaluation-import-2": {
          "container": {
            "args": [
              "--metrics",
              "{{$.inputs.artifacts['metrics'].path}}",
              "--metrics_explanation",
              "{{$.inputs.artifacts['metrics'].metadata['explanation_gcs_path']}}",
              "--explanation",
              "{{$.inputs.artifacts['explanation'].metadata['explanation_gcs_path']}}",
              "--problem_type",
              "{{$.inputs.parameters['problem_type']}}",
              "--display_name",
              "{{$.inputs.parameters['display_name']}}",
              "--dataset_path",
              "{{$.inputs.parameters['dataset_path']}}",
              "--dataset_type",
              "{{$.inputs.parameters['dataset_type']}}",
              "--pipeline_job_id",
              "{{$.pipeline_job_uuid}}",
              "--model_name",
              "{{$.inputs.artifacts['model'].metadata['resourceName']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.experimental.evaluation.import_model_evaluation"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.10"
          }
        },
        "exec-model-upload": {
          "container": {
            "args": [
              "--type",
              "UploadModel",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['display_name']}}\", \"description\": \"{{$.inputs.parameters['description']}}\", \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"explanation_metadata_artifact\": \"{{$.inputs.artifacts['explanation_metadata_artifact'].uri}}\", \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}, \"labels\": {{$.inputs.parameters['labels']}}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "launcher"
            ],
            "image": "gcr.io/ml-pipeline/automl-tables-private:1.0.11"
          }
        },
        "exec-model-upload-2": {
          "container": {
            "args": [
              "--type",
              "UploadModel",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['display_name']}}\", \"description\": \"{{$.inputs.parameters['description']}}\", \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"explanation_metadata_artifact\": \"{{$.inputs.artifacts['explanation_metadata_artifact'].uri}}\", \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}, \"labels\": {{$.inputs.parameters['labels']}}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "launcher"
            ],
            "image": "gcr.io/ml-pipeline/automl-tables-private:1.0.11"
          }
        },
        "exec-model-upload-3": {
          "container": {
            "args": [
              "--type",
              "UploadModel",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['display_name']}}\", \"description\": \"{{$.inputs.parameters['description']}}\", \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"explanation_metadata_artifact\": \"{{$.inputs.artifacts['explanation_metadata_artifact'].uri}}\", \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}, \"labels\": {{$.inputs.parameters['labels']}}}",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--executor_input",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "launcher"
            ],
            "image": "gcr.io/ml-pipeline/automl-tables-private:1.0.11"
          }
        },
        "exec-read-input-uri": {
          "container": {
            "args": [
              "--split-uri",
              "{{$.inputs.artifacts['split_uri'].path}}",
              "----output-paths",
              "{{$.outputs.parameters['Output'].output_file}}"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
              "def _read_input_uri(split_uri):\n    \"\"\"Construct Dataset based on the batch prediction job.\n\n    Args:\n      split_uri: Tbe path to the file that contains Dataset data.\n\n    Returns:\n      The list of string that represents the batch prediction input files.\n    \"\"\"\n    # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    import json\n\n    # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    with open(split_uri, \"r\") as f:\n        data_source = json.loads(f.read())\n        return data_source[\"tf_record_data_source\"][\"file_patterns\"]\n\ndef _serialize_json(obj) -> str:\n    if isinstance(obj, str):\n        return obj\n    import json\n\n    def default_serializer(obj):\n        if hasattr(obj, 'to_struct'):\n            return obj.to_struct()\n        else:\n            raise TypeError(\n                \"Object of type '%s' is not JSON serializable and does not have .to_struct() method.\"\n                % obj.__class__.__name__)\n\n    return json.dumps(obj, default=default_serializer, sort_keys=True)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Read input uri', description='Construct Dataset based on the batch prediction job.')\n_parser.add_argument(\"--split-uri\", dest=\"split_uri\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = _read_input_uri(**_parsed_args)\n\n_outputs = [_outputs]\n\n_output_serializers = [\n    _serialize_json,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n    try:\n        os.makedirs(os.path.dirname(output_file))\n    except OSError:\n        pass\n    with open(output_file, 'w') as f:\n        f.write(_output_serializers[idx](_outputs[idx]))\n"
            ],
            "image": "python:3.7-slim"
          }
        },
        "exec-read-input-uri-2": {
          "container": {
            "args": [
              "--split-uri",
              "{{$.inputs.artifacts['split_uri'].path}}",
              "----output-paths",
              "{{$.outputs.parameters['Output'].output_file}}"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
              "def _read_input_uri(split_uri):\n    \"\"\"Construct Dataset based on the batch prediction job.\n\n    Args:\n      split_uri: Tbe path to the file that contains Dataset data.\n\n    Returns:\n      The list of string that represents the batch prediction input files.\n    \"\"\"\n    # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    import json\n\n    # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    with open(split_uri, \"r\") as f:\n        data_source = json.loads(f.read())\n        return data_source[\"tf_record_data_source\"][\"file_patterns\"]\n\ndef _serialize_json(obj) -> str:\n    if isinstance(obj, str):\n        return obj\n    import json\n\n    def default_serializer(obj):\n        if hasattr(obj, 'to_struct'):\n            return obj.to_struct()\n        else:\n            raise TypeError(\n                \"Object of type '%s' is not JSON serializable and does not have .to_struct() method.\"\n                % obj.__class__.__name__)\n\n    return json.dumps(obj, default=default_serializer, sort_keys=True)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Read input uri', description='Construct Dataset based on the batch prediction job.')\n_parser.add_argument(\"--split-uri\", dest=\"split_uri\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = _read_input_uri(**_parsed_args)\n\n_outputs = [_outputs]\n\n_output_serializers = [\n    _serialize_json,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n    try:\n        os.makedirs(os.path.dirname(output_file))\n    except OSError:\n        pass\n    with open(output_file, 'w') as f:\n        f.write(_output_serializers[idx](_outputs[idx]))\n"
            ],
            "image": "python:3.7-slim"
          }
        },
        "exec-set-model-can-skip-validation": {
          "container": {
            "args": [
              "--executor_input",
              "{{$}}",
              "--function_to_execute",
              "_set_model_can_skip_validation"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main                         --component_module_path                         \"$program_path/ephemeral_component.py\"                         \"$@\"\n",
              "\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef _set_model_can_skip_validation(model: Input[Artifact]):\n    \"\"\"Construct Dataset based on the batch prediction job.\n\n    Args:\n      model: The model artifact.\n    \"\"\"\n    # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    import os\n    import tensorflow as tf\n\n    # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\n    # create an empty CAN_SKIP_VALIDATION file\n    with tf.io.gfile.GFile(os.path.join(model.uri, \"CAN_SKIP_VALIDATION\"), \"w\") as f:\n        f.write(\"\")\n\n"
            ],
            "image": "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:prod"
          }
        },
        "exec-tabular-stats-and-example-gen": {
          "container": {
            "args": [
              "--type",
              "CustomJob",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--payload",
              "{\"display_name\": \"tabular-stats-and-example-gen-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}\", \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:prod\", \"args\": [\"stats_generator\",\"--train_spec={\\\"prediction_type\\\": \\\"{{$.inputs.parameters['prediction_type']}}\\\", \\\"target_column\\\": \\\"{{$.inputs.parameters['target_column_name']}}\\\", \\\"optimization_objective\\\": \\\"{{$.inputs.parameters['optimization_objective']}}\\\", \\\"weight_column_name\\\": \\\"{{$.inputs.parameters['weight_column_name']}}\\\", \\\"transformations\\\": {{$.inputs.parameters['transformations']}}}\", \"--transformations_override_path={{$.inputs.parameters['transformations_path']}}\", \"--split_spec={{$.inputs.parameters['split_spec']}}\", \"--data_source={{$.inputs.parameters['data_source']}}\", \"--data_source_csv_filenames={{$.inputs.parameters['data_source_csv_filenames']}}\", \"--data_source_bigquery_table_path={{$.inputs.parameters['data_source_bigquery_table_path']}}\", \"--predefined_split_key={{$.inputs.parameters['predefined_split_key']}}\", \"--timestamp_split_key={{$.inputs.parameters['timestamp_split_key']}}\", \"--stratified_split_key={{$.inputs.parameters['stratified_split_key']}}\", \"--training_fraction={{$.inputs.parameters['training_fraction']}}\", \"--validation_fraction={{$.inputs.parameters['validation_fraction']}}\", \"--test_fraction={{$.inputs.parameters['test_fraction']}}\", \"--target_column={{$.inputs.parameters['target_column_name']}}\", \"--request_type={{$.inputs.parameters['request_type']}}\", \"--optimization_objective_recall_value={{$.inputs.parameters['optimization_objective_recall_value']}}\", \"--optimization_objective_precision_value={{$.inputs.parameters['optimization_objective_precision_value']}}\", \"--example_gen_gcs_output_prefix={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/example_gen_output\", \"--dataset_stats_dir={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/stats/\", \"--stats_result_path={{$.outputs.artifacts['dataset_stats'].uri}}\", \"--dataset_schema_path={{$.outputs.artifacts['dataset_schema'].uri}}\", \"--job_name=tabular-stats-and-example-gen-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}\", \"--dataflow_project={{$.inputs.parameters['project']}}\", \"--error_file_path={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--dataflow_staging_dir={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_staging\", \"--dataflow_tmp_dir={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers={{$.inputs.parameters['dataflow_max_num_workers']}}\", \"--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:prod\", \"--dataflow_machine_type={{$.inputs.parameters['dataflow_machine_type']}}\", \"--dataflow_disk_size_gb={{$.inputs.parameters['dataflow_disk_size_gb']}}\", \"--dataflow_kms_key={{$.inputs.parameters['encryption_spec_key_name']}}\", \"--dataflow_subnetwork_fully_qualified={{$.inputs.parameters['dataflow_subnetwork']}}\", \"--dataflow_use_public_ips={{$.inputs.parameters['dataflow_use_public_ips']}}\", \"--dataflow_service_account={{$.inputs.parameters['dataflow_service_account']}}\", \"--is_distill={{$.inputs.parameters['run_distillation']}}\", \"--additional_experiments={{$.inputs.parameters['additional_experiments']}}\", \"--metadata_path={{$.outputs.artifacts['metadata'].uri}}\", \"--train_split={{$.outputs.artifacts['train_split'].uri}}\", \"--eval_split={{$.outputs.artifacts['eval_split'].uri}}\", \"--test_split={{$.outputs.artifacts['test_split'].uri}}\", \"--test_split_for_batch_prediction_component={{$.outputs.parameters['test_split_json'].output_file}}\", \"--downsampled_test_split_for_batch_prediction_component={{$.outputs.parameters['downsampled_test_split_json'].output_file}}\", \"--instance_baseline_path={{$.outputs.artifacts['instance_baseline'].uri}}\", \"--parse_json=true\", \"--generate_additional_downsample_test_split=true\", \"--executor_input={{$.json_escape[1]}}\"]}}]}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.v1.gcp_launcher.launcher"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.10"
          }
        },
        "exec-write-bp-result-path": {
          "container": {
            "args": [
              "--executor_input",
              "{{$}}",
              "--function_to_execute",
              "_write_bp_result_path"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main                         --component_module_path                         \"$program_path/ephemeral_component.py\"                         \"$@\"\n",
              "\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef _write_bp_result_path(\n    bp_job: Input[Artifact],\n    result: OutputPath(\"Dataset\"),\n):\n    \"\"\"Construct Dataset based on the batch prediction job.\n\n    Args:\n      bp_job: The batch prediction job artifact.\n      result: Tbe path to the file that contains Dataset data.\n    \"\"\"\n    # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    import json\n\n    # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    directory = bp_job.metadata[\"gcsOutputDirectory\"]\n    data_source = {\n        \"tf_record_data_source\": {\n            \"file_patterns\": [\n                f\"{directory}/prediction.results-*\",\n            ],\n            \"coder\": \"PROTO_VALUE\",\n        },\n    }\n    with open(result, \"w\") as f:\n        f.write(json.dumps(data_source))\n\n"
            ],
            "image": "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:prod"
          }
        },
        "exec-write-bp-result-path-2": {
          "container": {
            "args": [
              "--executor_input",
              "{{$}}",
              "--function_to_execute",
              "_write_bp_result_path"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main                         --component_module_path                         \"$program_path/ephemeral_component.py\"                         \"$@\"\n",
              "\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef _write_bp_result_path(\n    bp_job: Input[Artifact],\n    result: OutputPath(\"Dataset\"),\n):\n    \"\"\"Construct Dataset based on the batch prediction job.\n\n    Args:\n      bp_job: The batch prediction job artifact.\n      result: Tbe path to the file that contains Dataset data.\n    \"\"\"\n    # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    import json\n\n    # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n    directory = bp_job.metadata[\"gcsOutputDirectory\"]\n    data_source = {\n        \"tf_record_data_source\": {\n            \"file_patterns\": [\n                f\"{directory}/prediction.results-*\",\n            ],\n            \"coder\": \"PROTO_VALUE\",\n        },\n    }\n    with open(result, \"w\") as f:\n        f.write(json.dumps(data_source))\n\n"
            ],
            "image": "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:prod"
          }
        }
      }
    },
    "pipelineInfo": {
      "name": "automl-tabular-deprecated"
    },
    "root": {
      "dag": {
        "outputs": {
          "artifacts": {
            "model-evaluation-2-evaluation_metrics": {
              "artifactSelectors": [
                {
                  "outputArtifactKey": "model-evaluation-2-evaluation_metrics",
                  "producerSubtask": "exit-handler-1"
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              ]
            },
            "model-evaluation-3-evaluation_metrics": {
              "artifactSelectors": [
                {
                  "outputArtifactKey": "model-evaluation-3-evaluation_metrics",
                  "producerSubtask": "exit-handler-1"
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            },
            "model-evaluation-4-evaluation_metrics": {
              "artifactSelectors": [
                {
                  "outputArtifactKey": "model-evaluation-4-evaluation_metrics",
                  "producerSubtask": "exit-handler-1"
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            "model-evaluation-evaluation_metrics": {
              "artifactSelectors": [
                {
                  "outputArtifactKey": "model-evaluation-evaluation_metrics",
                  "producerSubtask": "exit-handler-1"
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        "tasks": {
          "automl-tabular-finalizer": {
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            "dependentTasks": [
              "exit-handler-1"
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            "inputs": {
              "parameters": {
                "encryption_spec_key_name": {
                  "runtimeValue": {
                    "constantValue": {
                      "stringValue": ""
                    }
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                },
                "location": {
                  "componentInputParameter": "location"
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                "project": {
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                },
                "root_dir": {
                  "componentInputParameter": "root_dir"
                }
              }
            },
            "taskInfo": {
              "name": "automl-tabular-finalizer"
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            "triggerPolicy": {
              "strategy": "ALL_UPSTREAM_TASKS_COMPLETED"
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          "exit-handler-1": {
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            "inputs": {
              "parameters": {
                "pipelineparam--additional_experiments": {
                  "componentInputParameter": "additional_experiments"
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                "pipelineparam--data_source": {
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}