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: Generate the default mini_batch_size
code: def _get_default_mini_batch_size(self, num_records: int):
      """Generate the default mini_batch_size"""
      if is_pipeline_variable(self.instance_count):
          logger.warning(
              "mini_batch_size is not given in .fit() and instance_count is a "
              "pipeline variable (%s) which is only interpreted in pipeline execution time. "
              "Thus setting mini_batch_size to 1, since it can't be greater than "
              "number of records per instance_count, otherwise the training job fails.",
              type(self.instance_count),
          )
          return 1
  
      return min(
          self.DEFAULT_MINI_BATCH_SIZE,
          max(1, int(num_records / self.instance_count)),
      )
  
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 11 for calling the AmazonAlgorithmEstimatorBase obj's underlying member method code with example usage: obj._get_default_mini_batch_size(num_records) and returns: min, int