class_docstr: Base class for Amazon first-party Estimator implementations.
  
  This class isn't intended to be instantiated directly.
class_docstr_tok: ["Base","class","for","Amazon","first-party","Estimator","implementations",".","This","class","is","n't","intended","to","be","instantiated","directly","."]
docstr: Calls _prepare_for_training. Used when setting up a workflow.
  
  Args:
      records (:class:`~RecordSet`): The records to train this ``Estimator`` on.
      mini_batch_size (int or None): The size of each mini-batch to use when
          training. If ``None``, a default value will be used.
      job_name (str): Name of the training job to be created. If not
          specified, one is generated, using the base name given to the
          constructor if applicable.
code: def prepare_workflow_for_training(
      self, records=None, mini_batch_size=None, job_name=None
  ):
      """Calls _prepare_for_training. Used when setting up a workflow.
  
      Args:
          records (:class:`~RecordSet`): The records to train this ``Estimator`` on.
          mini_batch_size (int or None): The size of each mini-batch to use when
              training. If ``None``, a default value will be used.
          job_name (str): Name of the training job to be created. If not
              specified, one is generated, using the base name given to the
              constructor if applicable.
      """
      self._prepare_for_training(
          records=records,
          mini_batch_size=mini_batch_size,
          job_name=job_name,
      )
  
imports: from __future__ import absolute_import
  import json
  import logging
  import tempfile
  from typing import Union, Optional, Dict
  from six.moves.urllib.parse import urlparse
  from sagemaker import image_uris, s3_utils
  from sagemaker.amazon import validation
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.common import write_numpy_to_dense_tensor
  from sagemaker.deprecations import renamed_warning
  from sagemaker.estimator import EstimatorBase, _TrainingJob
  from sagemaker.inputs import FileSystemInput, TrainingInput
  from sagemaker.utils import sagemaker_timestamp, check_and_get_run_experiment_config
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 7 for calling the AmazonAlgorithmEstimatorBase obj's underlying member method code with example usage: obj.prepare_workflow_for_training(records, mini_batch_size, job_name) without return types