class_docstr: Create Auto ML Tabular Training job.
code: def __init__(
      self,
      *,
      dataset_id: str,
      target_column: str,
      optimization_prediction_type: str,
      optimization_objective: str | None = None,
      column_specs: dict[str, str] | None = None,
      column_transformations: list[dict[str, dict[str, str]]]
      | None = None,
      optimization_objective_recall_value: float | None = None,
      optimization_objective_precision_value: float | None = None,
      validation_fraction_split: float | None = None,
      predefined_split_column_name: str | None = None,
      timestamp_split_column_name: str | None = None,
      weight_column: str | None = None,
      budget_milli_node_hours: int = 1000,
      disable_early_stopping: bool = False,
      export_evaluated_data_items: bool = False,
      export_evaluated_data_items_bigquery_destination_uri: str
      | None = None,
      export_evaluated_data_items_override_destination: bool = False,
      **kwargs,
  ) -> None:
      super().__init__(**kwargs)
      self.dataset_id = dataset_id
      self.target_column = target_column
      self.optimization_prediction_type = optimization_prediction_type
      self.optimization_objective = optimization_objective
      self.column_specs = column_specs
      self.column_transformations = column_transformations
      self.optimization_objective_recall_value = (
          optimization_objective_recall_value
      )
      self.optimization_objective_precision_value = (
          optimization_objective_precision_value
      )
      self.validation_fraction_split = validation_fraction_split
      self.predefined_split_column_name = predefined_split_column_name
      self.timestamp_split_column_name = timestamp_split_column_name
      self.weight_column = weight_column
      self.budget_milli_node_hours = budget_milli_node_hours
      self.disable_early_stopping = disable_early_stopping
      self.export_evaluated_data_items = export_evaluated_data_items
      self.export_evaluated_data_items_bigquery_destination_uri = (
          export_evaluated_data_items_bigquery_destination_uri
      )
      self.export_evaluated_data_items_override_destination = (
          export_evaluated_data_items_override_destination
      )
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Sequence
  from google.api_core.exceptions import NotFound
  from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
  from google.cloud.aiplatform import datasets
  from google.cloud.aiplatform.models import Model
  from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
  from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
  from airflow.providers.google.cloud.links.vertex_ai import VertexAIModelLink, VertexAITrainingLink, VertexAITrainingPipelinesLink
  from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
label_desc: Use image node_id 1 to create a new CreateAutoMLTabularTrainingJobOperator object from inherited base classes: AutoMLTrainingJobBaseOperator with example: obj = CreateAutoMLTabularTrainingJobOperator()