class_docstr: An unsupervised algorithm for detecting anomalous data points within a data set.
  
  These are observations which diverge from otherwise well-structured or patterned data.
  Anomalies can manifest as unexpected spikes in time series data, breaks in periodicity,
  or unclassifiable data points.
docstr: Placeholder docstring
docstr_tok: ["Placeholder","docstring"]
code: def _prepare_for_training(
      self, records, mini_batch_size=None, job_name=None
  ):
      """Placeholder docstring"""
      if mini_batch_size is None:
          mini_batch_size = self.MINI_BATCH_SIZE
      elif mini_batch_size != self.MINI_BATCH_SIZE:
          raise ValueError(
              "Random Cut Forest uses a fixed mini_batch_size of {}".format(
                  self.MINI_BATCH_SIZE
              )
          )
  
      super(RandomCutForest, self)._prepare_for_training(
          records, mini_batch_size=mini_batch_size, job_name=job_name
      )
  
imports: from __future__ import absolute_import
  from typing import Optional, Union, List
  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 ge, le
  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
label_desc: Use image node_id 3 for calling the RandomCutForest obj's underlying member method code with example usage: obj._prepare_for_training(records, mini_batch_size, job_name) without return types