class_docstr: A class for creating and interacting with SageMaker AutoML jobs.
docstr: Create an AutoML Job with the input dataset.
  
  Args:
      inputs (str or list[str] or AutoMLInput or list[AutoMLInput]):
          Local path or S3 Uri where the training data is stored.
          Or an AutoMLInput object. Or a list of AutoMLInput objects.
          If a local path is provided, the dataset will be uploaded to an S3 location.
          The list of AutoMLInput objects is to specify the training or the validation
          input source. Input source for training and validation
          must share the same content type and target attribute name.
          Minimum number of 1 item. Maximum number of 2 items for list[AutoMLInput].
      wait (bool): Whether the call should wait until the job completes (default: True).
      logs (bool): Whether to show the logs produced by the job. Only meaningful when wait
          is True (default: True). if ``wait`` is False, ``logs`` will be set to False as
          well.
      job_name (str): Training job name. If not specified, the estimator generates
          a default job name, based on the training image name and current timestamp.
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code: def fit(self, inputs=None, wait=True, logs=True, job_name=None):
      """Create an AutoML Job with the input dataset.
  
      Args:
          inputs (str or list[str] or AutoMLInput or list[AutoMLInput]):
              Local path or S3 Uri where the training data is stored.
              Or an AutoMLInput object. Or a list of AutoMLInput objects.
              If a local path is provided, the dataset will be uploaded to an S3 location.
              The list of AutoMLInput objects is to specify the training or the validation
              input source. Input source for training and validation
              must share the same content type and target attribute name.
              Minimum number of 1 item. Maximum number of 2 items for list[AutoMLInput].
          wait (bool): Whether the call should wait until the job completes (default: True).
          logs (bool): Whether to show the logs produced by the job. Only meaningful when wait
              is True (default: True). if ``wait`` is False, ``logs`` will be set to False as
              well.
          job_name (str): Training job name. If not specified, the estimator generates
              a default job name, based on the training image name and current timestamp.
      """
      if not wait and logs:
          logs = False
          logger.warning(
              "Setting logs to False. logs is only meaningful when wait is True."
          )
  
      # upload data for users if provided local path
      # validations are done in _Job._format_inputs_to_input_config
      if isinstance(inputs, string_types):
          if not inputs.startswith("s3://"):
              inputs = self.sagemaker_session.upload_data(
                  inputs, key_prefix="auto-ml-input-data"
              )
      self._prepare_for_auto_ml_job(job_name=job_name)
  
      self.latest_auto_ml_job = AutoMLJob.start_new(
          self, inputs
      )  # pylint: disable=W0201
      if wait:
          self.latest_auto_ml_job.wait(logs=logs)
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, List, Dict
  from six import string_types
  from sagemaker import Model, PipelineModel, s3
  from sagemaker.automl.candidate_estimator import CandidateEstimator
  from sagemaker.config import AUTO_ML_ROLE_ARN_PATH, AUTO_ML_KMS_KEY_ID_PATH, AUTO_ML_VPC_CONFIG_PATH, AUTO_ML_VOLUME_KMS_KEY_ID_PATH, AUTO_ML_INTER_CONTAINER_ENCRYPTION_PATH
  from sagemaker.job import _Job
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, resolve_value_from_config, format_tags, Tags
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
label_desc: Use image node_id 2 for calling the AutoML obj's underlying member method code with example usage: obj.fit(inputs, wait, logs, job_name) without return types