class_docstr: A general-purpose neural embedding algorithm that is highly customizable.
  
  It can learn low-dimensional dense embeddings of high-dimensional objects. The embeddings
  are learned in a way that preserves the semantics of the relationship between pairs of
  objects in the original space in the embedding space.
class_docstr_tok: ["A","general-purpose","neural","embedding","algorithm","that","is","highly","customizable",".","It","can","learn","low-dimensional","dense","embeddings","of","high-dimensional","objects",".","The","embeddings","are","learned","in","a","way","that","preserves","the","semantics","of","the","relationship","between","pairs","of","objects","in","the","original","space","in","the","embedding","space","."]
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
  
      super(Object2Vec, self)._prepare_for_training(
          records, mini_batch_size=mini_batch_size, job_name=job_name
      )
  
imports: from __future__ import absolute_import
  from typing import Union, Optional
  from sagemaker import image_uris
  from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.validation import ge, le, isin
  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 Object2Vec obj's underlying member method code with example usage: obj._prepare_for_training(records, mini_batch_size, job_name) without return types