class_docstr: An unsupervised learning algorithm that learns the usage patterns for IPv4 addresses.
  
  It is designed to capture associations between IPv4 addresses and various entities, such
  as user IDs or account numbers.
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 not None and (
          mini_batch_size < 1 or mini_batch_size > 500000
      ):
          raise ValueError("mini_batch_size must be in [1, 500000]")
      super(IPInsights, 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
  from sagemaker.deserializers import JSONDeserializer
  from sagemaker.predictor import Predictor
  from sagemaker.model import Model
  from sagemaker.serializers import CSVSerializer
  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 IPInsights obj's underlying member method code with example usage: obj._prepare_for_training(records, mini_batch_size, job_name) without return types