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              "--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",
              "{{$}}"
            ],
            "command": [
              "python",
              "/main.py"
            ],
            "image": "gcr.io/ml-pipeline/model-evaluation:v0.4"
          }
        },
        "exec-model-upload": {
          "container": {
            "args": [
              "--type",
              "UploadModel",
              "--payload",
              "{\"display_name\": \"{{$.inputs.parameters['display_name']}}\", \"description\": \"{{$.inputs.parameters['description']}}\", \"explanation_spec\": {\"parameters\": {{$.inputs.parameters['explanation_parameters']}}, \"metadata\": {{$.inputs.parameters['explanation_metadata']}}}, \"encryption_spec\": {\"kms_key_name\":\"{{$.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",
              "{{$}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "launcher"
            ],
            "image": "gcr.io/ml-pipeline/automl-tables-private:1.0.13"
          }
        },
        "exec-parse-worker-pool-specs-override": {
          "container": {
            "args": [
              "--executor_input",
              "{{$}}",
              "--function_to_execute",
              "_parse_worker_pool_specs_override"
            ],
            "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 _parse_worker_pool_specs_override(\n    worker_pool_specs_override: list  # pylint:disable=g-bare-generic\n) -> NamedTuple(\n    'Outputs',\n    [\n        ('training_machine_spec', dict),  # pylint:disable=g-bare-generic\n        ('training_disk_spec', dict),\n        ('eval_machine_spec', dict),  # pylint:disable=g-bare-generic\n        ('eval_replica_count', int),\n    ]):\n  \"\"\"Parses worker_pool_specs_override and returns training and evaluation machine specifications.\n\n  Args:\n    worker_pool_specs_override: The list of dictionaries for overriding training\n      and evaluation worker pool specs.\n\n  Returns:\n      training_machine_spec: The training machine spec.\n      training_disk_spec: The training disk spec.\n      eval_machine_spec: The eval machine spec.\n      eval_replica_count: The replica count for eval.\n  \"\"\"\n  # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name\n  import collections\n  # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name\n\n  training_machine_spec = {'machine_type': 'c2-standard-16'}\n  training_disk_spec = {'boot_disk_type': 'pd-ssd', 'boot_disk_size_gb': 100}\n  eval_machine_spec = {'machine_type': 'c2-standard-8'}\n  eval_replica_count = 1\n\n  if worker_pool_specs_override:\n    if len(worker_pool_specs_override) >= 1 and isinstance(\n        worker_pool_specs_override[0], dict):\n      training_machine_spec = worker_pool_specs_override[0].get(\n          'machine_spec', training_machine_spec)\n      training_disk_spec = worker_pool_specs_override[0].get(\n          'disk_spec', training_disk_spec)\n    if len(worker_pool_specs_override) == 4 and isinstance(\n        worker_pool_specs_override[3], dict):\n      eval_machine_spec = worker_pool_specs_override[3].get(\n          'machine_spec', eval_machine_spec)\n      eval_replica_count = worker_pool_specs_override[3].get(\n          'replica_count', eval_replica_count)\n\n  return collections.namedtuple('Outputs', [\n      'training_machine_spec', 'training_disk_spec', 'eval_machine_spec',\n      'eval_replica_count'\n  ])(training_machine_spec, training_disk_spec, eval_machine_spec,\n     eval_replica_count)\n\n"
            ],
            "image": "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20230123_2125"
          }
        },
        "exec-split-materialized-data": {
          "container": {
            "args": [
              "--executor_input",
              "{{$}}",
              "--function_to_execute",
              "_split_materialized_data"
            ],
            "command": [
              "sh",
              "-ec",
              "program_path=$(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 _split_materialized_data(\n    materialized_data: Input[Dataset],\n    materialized_train_split: OutputPath('MaterializedSplit'),\n    materialized_eval_split: OutputPath('MaterializedSplit'),\n    materialized_test_split: OutputPath('MaterializedSplit')):\n  \"\"\"Splits materialized_data into materialized_data test, train, and eval splits.\n\n  Necessary adapter between FTE pipeline and trainer.\n\n  Args:\n    materialized_data: materialized_data dataset output by FTE.\n    materialized_train_split: Path patern to materialized_train_split.\n    materialized_eval_split: Path patern to materialized_eval_split.\n    materialized_test_split: Path patern to materialized_test_split.\n  \"\"\"\n  # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n  import json\n  import tensorflow as tf\n  # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\n  with tf.io.gfile.GFile(materialized_data.path, 'r') as f:\n    artifact_path = f.read()\n\n  # needed to import tf because this is a path in gs://\n  with tf.io.gfile.GFile(artifact_path, 'r') as f:\n    materialized_data_json = json.load(f)\n\n  file_patterns = materialized_data_json['tf_record_data_source'][\n      'file_patterns']\n\n  # we map indices to file patterns based on the ordering of insertion order\n  # in our transform_data (see above in _generate_analyze_and_transform_data)\n  with tf.io.gfile.GFile(materialized_train_split, 'w') as f:\n    f.write(file_patterns[0])\n\n  with tf.io.gfile.GFile(materialized_eval_split, 'w') as f:\n    f.write(file_patterns[1])\n\n  with tf.io.gfile.GFile(materialized_test_split, 'w') as f:\n    f.write(file_patterns[2])\n\n"
            ],
            "image": "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20230123_2125"
          }
        },
        "exec-training-configurator-and-validator": {
          "container": {
            "args": [
              "training_configurator_and_validator",
              "--instance_schema_path={{$.inputs.artifacts['instance_schema'].uri}}",
              "--training_schema_path={{$.inputs.artifacts['training_schema'].uri}}",
              "--dataset_stats_path={{$.inputs.artifacts['dataset_stats'].uri}}",
              "--split_example_counts={{$.inputs.parameters['split_example_counts']}}",
              "--target_column={{$.inputs.parameters['target_column']}}",
              "--weight_column={{$.inputs.parameters['weight_column']}}",
              "--prediction_type={{$.inputs.parameters['prediction_type']}}",
              "--optimization_objective={{$.inputs.parameters['optimization_objective']}}",
              "--optimization_objective_recall_value={{$.inputs.parameters['optimization_objective_recall_value']}}",
              "--optimization_objective_precision_value={{$.inputs.parameters['optimization_objective_precision_value']}}",
              "--metadata_path={{$.outputs.artifacts['metadata'].uri}}",
              "--instance_baseline_path={{$.outputs.artifacts['instance_baseline'].uri}}",
              "--run_evaluation={{$.inputs.parameters['run_evaluation']}}",
              "--run_distill={{$.inputs.parameters['run_distill']}}",
              "--enable_probabilistic_inference={{$.inputs.parameters['enable_probabilistic_inference']}}",
              "--time_series_identifier_column={{$.inputs.parameters['time_series_identifier_column']}}",
              "--time_column={{$.inputs.parameters['time_column']}}",
              "--time_series_attribute_columns={{$.inputs.parameters['time_series_attribute_columns']}}",
              "--available_at_forecast_columns={{$.inputs.parameters['available_at_forecast_columns']}}",
              "--unavailable_at_forecast_columns={{$.inputs.parameters['unavailable_at_forecast_columns']}}",
              "--quantiles={{$.inputs.parameters['quantiles']}}",
              "--context_window={{$.inputs.parameters['context_window']}}",
              "--forecast_horizon={{$.inputs.parameters['forecast_horizon']}}",
              "--forecasting_model_type={{$.inputs.parameters['forecasting_model_type']}}",
              "--forecasting_transformations_path={{$.inputs.parameters['forecasting_transformations_path']}}"
            ],
            "image": "us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20230123_2125"
          }
        },
        "exec-wide-and-deep-trainer": {
          "container": {
            "args": [
              "--type",
              "CustomJob",
              "--project",
              "{{$.inputs.parameters['project']}}",
              "--location",
              "{{$.inputs.parameters['location']}}",
              "--gcp_resources",
              "{{$.outputs.parameters['gcp_resources'].output_file}}",
              "--payload",
              "{\"display_name\": \"wide-and-deep-trainer-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}\", \"encryption_spec\": {\"kms_key_name\":\"{{$.inputs.parameters['encryption_spec_key_name']}}\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\":\"1\", \"machine_spec\": {{$.inputs.parameters['training_machine_spec']}}, \"disk_spec\": {{$.inputs.parameters['training_disk_spec']}}, \"container_spec\": {\"image_uri\":\"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20230123_2125\", \"args\": [\"--target_column={{$.inputs.parameters['target_column']}}\", \"--weight_column={{$.inputs.parameters['weight_column']}}\", \"--model_type={{$.inputs.parameters['prediction_type']}}\", \"--prediction_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20230123_2125\", \"--baseline_path={{$.inputs.artifacts['instance_baseline'].uri}}\", \"--metadata_path={{$.inputs.artifacts['metadata'].uri}}\", \"--transform_output_path={{$.inputs.artifacts['transform_output'].uri}}\", \"--training_schema_path={{$.inputs.artifacts['training_schema_uri'].uri}}\", \"--job_dir={{$.inputs.parameters['root_dir']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train\", \"--training_data_path={{$.inputs.artifacts['materialized_train_split'].uri}}\", \"--validation_data_path={{$.inputs.artifacts['materialized_eval_split'].uri}}\", \"--max_steps={{$.inputs.parameters['max_steps']}}\", \"--max_train_secs={{$.inputs.parameters['max_train_secs']}}\", \"--learning_rate={{$.inputs.parameters['learning_rate']}}\", \"--optimizer_type={{$.inputs.parameters['optimizer_type']}}\", \"--l1_regularization_strength={{$.inputs.parameters['l1_regularization_strength']}}\", \"--l2_regularization_strength={{$.inputs.parameters['l2_regularization_strength']}}\", \"--l2_shrinkage_regularization_strength={{$.inputs.parameters['l2_shrinkage_regularization_strength']}}\", \"--beta_1={{$.inputs.parameters['beta_1']}}\", \"--beta_2={{$.inputs.parameters['beta_2']}}\", \"--hidden_units={{$.inputs.parameters['hidden_units']}}\", \"--use_wide={{$.inputs.parameters['use_wide']}}\", \"--embed_categories={{$.inputs.parameters['embed_categories']}}\", \"--dnn_dropout={{$.inputs.parameters['dnn_dropout']}}\", \"--dnn_learning_rate={{$.inputs.parameters['dnn_learning_rate']}}\", \"--dnn_optimizer_type={{$.inputs.parameters['dnn_optimizer_type']}}\", \"--dnn_l1_regularization_strength={{$.inputs.parameters['dnn_l1_regularization_strength']}}\", \"--dnn_l2_regularization_strength={{$.inputs.parameters['dnn_l2_regularization_strength']}}\", \"--dnn_l2_shrinkage_regularization_strength={{$.inputs.parameters['dnn_l2_shrinkage_regularization_strength']}}\", \"--dnn_beta_1={{$.inputs.parameters['dnn_beta_1']}}\", \"--dnn_beta_2={{$.inputs.parameters['dnn_beta_2']}}\", \"--enable_profiler={{$.inputs.parameters['enable_profiler']}}\", \"--cache_data={{$.inputs.parameters['cache_data']}}\", \"--seed={{$.inputs.parameters['seed']}}\", \"--eval_steps={{$.inputs.parameters['eval_steps']}}\", \"--batch_size={{$.inputs.parameters['batch_size']}}\", \"--measurement_selection_type={{$.inputs.parameters['measurement_selection_type']}}\", \"--optimization_metric={{$.inputs.parameters['optimization_metric']}}\", \"--eval_frequency_secs={{$.inputs.parameters['eval_frequency_secs']}}\", \"--executor_input={{$.json_escape[1]}}\"]}}]}}"
            ],
            "command": [
              "python3",
              "-u",
              "-m",
              "google_cloud_pipeline_components.container.v1.custom_job.launcher"
            ],
            "image": "gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.32"
          }
        }
      }
    },
    "pipelineInfo": {
      "name": "automl-tabular-wide-and-deep-trainer"
    },
    "root": {
      "dag": {
        "outputs": {
          "artifacts": {
            "model-evaluation-evaluation_metrics": {
              "artifactSelectors": [
                {
                  "outputArtifactKey": "model-evaluation-evaluation_metrics",
                  "producerSubtask": "exit-handler-1"
                }
              ]
            }
          }
        },
        "tasks": {
          "automl-tabular-finalizer": {
            "componentRef": {
              "name": "comp-automl-tabular-finalizer"
            },
            "dependentTasks": [
              "exit-handler-1"
            ],
            "inputs": {
              "parameters": {
                "encryption_spec_key_name": {
                  "runtimeValue": {
                    "constantValue": {
                      "stringValue": ""
                    }
                  }
                },
                "location": {
                  "componentInputParameter": "location"
                },
                "project": {
                  "componentInputParameter": "project"
                },
                "root_dir": {
                  "componentInputParameter": "root_dir"
                }
              }
            },
            "taskInfo": {
              "name": "automl-tabular-finalizer"
            },
            "triggerPolicy": {
              "strategy": "ALL_UPSTREAM_TASKS_COMPLETED"
            }
          },
          "exit-handler-1": {
            "componentRef": {
              "name": "comp-exit-handler-1"
            },
            "inputs": {
              "parameters": {
                "pipelineparam--batch_size": {
                  "componentInputParameter": "batch_size"
                },
                "pipelineparam--beta_1": {
                  "componentInputParameter": "beta_1"
                },
                "pipelineparam--beta_2": {
                  "componentInputParameter": "beta_2"
                },
                "pipelineparam--bigquery_staging_full_dataset_id": {
                  "componentInputParameter": "bigquery_staging_full_dataset_id"
                },
                "pipelineparam--cache_data": {
                  "componentInputParameter": "cache_data"
                },
                "pipelineparam--data_source_bigquery_table_path": {
                  "componentInputParameter": "data_source_bigquery_table_path"
                },
                "pipelineparam--data_source_csv_filenames": {
                  "componentInputParameter": "data_source_csv_filenames"
                },
                "pipelineparam--dataflow_service_account": {
                  "componentInputParameter": "dataflow_service_account"
                },
                "pipelineparam--dataflow_subnetwork": {
                  "componentInputParameter": "dataflow_subnetwork"
                },
                "pipelineparam--dataflow_use_public_ips": {
                  "componentInputParameter": "dataflow_use_public_ips"
                },
                "pipelineparam--dataset_level_custom_transformation_definitions": {
                  "componentInputParameter": "dataset_level_custom_transformation_definitions"
                },
                "pipelineparam--dataset_level_transformations": {
                  "componentInputParameter": "dataset_level_transformations"
                },
                "pipelineparam--dnn_beta_1": {
                  "componentInputParameter": "dnn_beta_1"
                },
                "pipelineparam--dnn_beta_2": {
                  "componentInputParameter": "dnn_beta_2"
                },
                "pipelineparam--dnn_dropout": {
                  "componentInputParameter": "dnn_dropout"
                },
                "pipelineparam--dnn_l1_regularization_strength": {
                  "componentInputParameter": "dnn_l1_regularization_strength"
                },
                "pipelineparam--dnn_l2_regularization_strength": {
                  "componentInputParameter": "dnn_l2_regularization_strength"
                },
                "pipelineparam--dnn_l2_shrinkage_regularization_strength": {
                  "componentInputParameter": "dnn_l2_shrinkage_regularization_strength"
                },
                "pipelineparam--dnn_learning_rate": {
                  "componentInputParameter": "dnn_learning_rate"
                },
                "pipelineparam--dnn_optimizer_type": {
                  "componentInputParameter": "dnn_optimizer_type"
                },
                "pipelineparam--embed_categories": {
                  "componentInputParameter": "embed_categories"
                },
                "pipelineparam--enable_profiler": {
                  "componentInputParameter": "enable_profiler"
                },
                "pipelineparam--encryption_spec_key_name": {
                  "componentInputParameter": "encryption_spec_key_name"
                },
                "pipelineparam--eval_frequency_secs": {
                  "componentInputParameter": "eval_frequency_secs"
                },
                "pipelineparam--eval_steps": {
                  "componentInputParameter": "eval_steps"
                },
                "pipelineparam--evaluation_batch_predict_machine_type": {
                  "componentInputParameter": "evaluation_batch_predict_machine_type"
                },
                "pipelineparam--evaluation_batch_predict_max_replica_count": {
                  "componentInputParameter": "evaluation_batch_predict_max_replica_count"
                },
                "pipelineparam--evaluation_batch_predict_starting_replica_count": {
                  "componentInputParameter": "evaluation_batch_predict_starting_replica_count"
                },
                "pipelineparam--evaluation_dataflow_disk_size_gb": {
                  "componentInputParameter": "evaluation_dataflow_disk_size_gb"
                },
                "pipelineparam--evaluation_dataflow_machine_type": {
                  "componentInputParameter": "evaluation_dataflow_machine_type"
                },
                "pipelineparam--evaluation_dataflow_max_num_workers": {
                  "componentInputParameter": "evaluation_dataflow_max_num_workers"
                },
                "pipelineparam--feature_selection_algorithm": {
                  "componentInputParameter": "feature_selection_algorithm"
                },
                "pipelineparam--hidden_units": {
                  "componentInputParameter": "hidden_units"
                },
                "pipelineparam--l1_regularization_strength": {
                  "componentInputParameter": "l1_regularization_strength"
                },
                "pipelineparam--l2_regularization_strength": {
                  "componentInputParameter": "l2_regularization_strength"
                },
                "pipelineparam--l2_shrinkage_regularization_strength": {
                  "componentInputParameter": "l2_shrinkage_regularization_strength"
                },
                "pipelineparam--learning_rate": {
                  "componentInputParameter": "learning_rate"
                },
                "pipelineparam--location": {
                  "componentInputParameter": "location"
                },
                "pipelineparam--max_selected_features": {
                  "componentInputParameter": "max_selected_features"
                },
                "pipelineparam--max_steps": {
                  "componentInputParameter": "max_steps"
                },
                "pipelineparam--max_train_secs": {
                  "componentInputParameter": "max_train_secs"
                },
                "pipelineparam--measurement_selection_type": {
                  "componentInputParameter": "measurement_selection_type"
                },
                "pipelineparam--optimization_metric": {
                  "componentInputParameter": "optimization_metric"
                },
                "pipelineparam--optimizer_type": {
                  "componentInputParameter": "optimizer_type"
                },
                "pipelineparam--predefined_split_key": {
                  "componentInputParameter": "predefined_split_key"
                },
                "pipelineparam--prediction_type": {
                  "componentInputParameter": "prediction_type"
                },
                "pipelineparam--project": {
                  "componentInputParameter": "project"
                },
                "pipelineparam--root_dir": {
                  "componentInputParameter": "root_dir"
                },
                "pipelineparam--run_evaluation": {
                  "componentInputParameter": "run_evaluation"
                },
                "pipelineparam--run_feature_selection": {
                  "componentInputParameter": "run_feature_selection"
                },
                "pipelineparam--seed": {
                  "componentInputParameter": "seed"
                },
                "pipelineparam--stratified_split_key": {
                  "componentInputParameter": "stratified_split_key"
                },
                "pipelineparam--target_column": {
                  "componentInputParameter": "target_column"
                },
                "pipelineparam--test_fraction": {
                  "componentInputParameter": "test_fraction"
                },
                "pipelineparam--tf_auto_transform_features": {
                  "componentInputParameter": "tf_auto_transform_features"
                },
                "pipelineparam--tf_custom_transformation_definitions": {
                  "componentInputParameter": "tf_custom_transformation_definitions"
                },
                "pipelineparam--tf_transformations_path": {
                  "componentInputParameter": "tf_transformations_path"
                },
                "pipelineparam--training_fraction": {
                  "componentInputParameter": "training_fraction"
                },
                "pipelineparam--transform_dataflow_disk_size_gb": {
                  "componentInputParameter": "transform_dataflow_disk_size_gb"
                },
                "pipelineparam--transform_dataflow_machine_type": {
                  "componentInputParameter": "transform_dataflow_machine_type"
                },
                "pipelineparam--transform_dataflow_max_num_workers": {
                  "componentInputParameter": "transform_dataflow_max_num_workers"
                },
                "pipelineparam--use_wide": {
                  "componentInputParameter": "use_wide"
                },
                "pipelineparam--validation_fraction": {
                  "componentInputParameter": "validation_fraction"
                },
                "pipelineparam--weight_column": {
                  "componentInputParameter": "weight_column"
                },
                "pipelineparam--worker_pool_specs_override": {
                  "componentInputParameter": "worker_pool_specs_override"
                }
              }
            },
            "taskInfo": {
              "name": "exit-handler-1"
            }
          }
        }
      },
      "inputDefinitions": {
        "parameters": {
          "batch_size": {
            "type": "INT"
          },
          "beta_1": {
            "type": "DOUBLE"
          },
          "beta_2": {
            "type": "DOUBLE"
          },
          "bigquery_staging_full_dataset_id": {
            "type": "STRING"
          },
          "cache_data": {
            "type": "STRING"
          },
          "data_source_bigquery_table_path": {
            "type": "STRING"
          },
          "data_source_csv_filenames": {
            "type": "STRING"
          },
          "dataflow_service_account": {
            "type": "STRING"
          },
          "dataflow_subnetwork": {
            "type": "STRING"
          },
          "dataflow_use_public_ips": {
            "type": "STRING"
          },
          "dataset_level_custom_transformation_definitions": {
            "type": "STRING"
          },
          "dataset_level_transformations": {
            "type": "STRING"
          },
          "dnn_beta_1": {
            "type": "DOUBLE"
          },
          "dnn_beta_2": {
            "type": "DOUBLE"
          },
          "dnn_dropout": {
            "type": "DOUBLE"
          },
          "dnn_l1_regularization_strength": {
            "type": "DOUBLE"
          },
          "dnn_l2_regularization_strength": {
            "type": "DOUBLE"
          },
          "dnn_l2_shrinkage_regularization_strength": {
            "type": "DOUBLE"
          },
          "dnn_learning_rate": {
            "type": "DOUBLE"
          },
          "dnn_optimizer_type": {
            "type": "STRING"
          },
          "embed_categories": {
            "type": "STRING"
          },
          "enable_profiler": {
            "type": "STRING"
          },
          "encryption_spec_key_name": {
            "type": "STRING"
          },
          "eval_frequency_secs": {
            "type": "INT"
          },
          "eval_steps": {
            "type": "INT"
          },
          "evaluation_batch_predict_machine_type": {
            "type": "STRING"
          },
          "evaluation_batch_predict_max_replica_count": {
            "type": "INT"
          },
          "evaluation_batch_predict_starting_replica_count": {
            "type": "INT"
          },
          "evaluation_dataflow_disk_size_gb": {
            "type": "INT"
          },
          "evaluation_dataflow_machine_type": {
            "type": "STRING"
          },
          "evaluation_dataflow_max_num_workers": {
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