code: def _batch(
      input_dataset,
      batch_size,
      drop_remainder=False,
      num_parallel_calls=None,
      deterministic=None,
      name=None,
  ):
      """See `Dataset.batch` for details."""
      if num_parallel_calls is None or debug_mode.DEBUG_MODE:
          if deterministic is not None and not debug_mode.DEBUG_MODE:
              warnings.warn(
                  "The `deterministic` argument has no effect unless the "
                  "`num_parallel_calls` argument is specified."
              )
          return _BatchDataset(
              input_dataset, batch_size, drop_remainder, name=name
          )
      else:
          return _ParallelBatchDataset(
              input_dataset,
              batch_size,
              drop_remainder,
              num_parallel_calls,
              deterministic,
              name=name,
          )
  
file_path: tensorflow-upstream/tensorflow/python/data/ops/batch_op.py
imports: import warnings
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import debug_mode
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import gen_dataset_ops
label_desc: Use image node_id 1 for calling a global function with example usage: _batch(input_dataset, batch_size, drop_remainder, num_parallel_calls, deterministic, name) and returns: _BatchDataset, _ParallelBatchDataset