class_docstr: An unsupervised learning algorithm attempting to describe data as distinct categories.
  
  LDA is most commonly used to discover a
  user-specified number of topics shared by documents within a text corpus. Here each
  observation is a document, the features are the presence (or occurrence count) of each
  word, and the categories are the topics.
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docstr: Placeholder docstring
docstr_tok: ["Placeholder","docstring"]
code: def _prepare_for_training(  # pylint: disable=signature-differs
      self, records, mini_batch_size, job_name=None
  ):
      # mini_batch_size is required, prevent explicit calls with None
      """Placeholder docstring"""
      if mini_batch_size is None:
          raise ValueError("mini_batch_size must be set")
  
      super(LDA, self)._prepare_for_training(
          records, mini_batch_size=mini_batch_size, job_name=job_name
      )
  
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional
  from sagemaker import image_uris
  from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase
  from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.validation import gt
  from sagemaker.predictor import Predictor
  from sagemaker.model import Model
  from sagemaker.session import Session
  from sagemaker.utils import pop_out_unused_kwarg
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 3 for calling the LDA obj's underlying member method code with example usage: obj._prepare_for_training(records, mini_batch_size, job_name) without return types