code: def hparams_to_batching_scheme(
      hparams,
      drop_long_sequences=False,
      shard_multiplier=1,
      length_multiplier=1,
  ):
      """Wrapper around _batching_scheme with hparams."""
      return batching_scheme(
          batch_size=hparams.batch_size,
          min_length=hparams.min_length,
          max_length=hparams.max_length,
          min_length_bucket=hparams.min_length_bucket,
          length_bucket_step=hparams.length_bucket_step,
          drop_long_sequences=drop_long_sequences,
          shard_multiplier=shard_multiplier,
          length_multiplier=length_multiplier,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import multiprocessing
  import random
  import six
  from six.moves import range
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import mlperf_log
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 7 for calling a global function with example usage: hparams_to_batching_scheme(hparams, drop_long_sequences, shard_multiplier, length_multiplier) and returns: batching_scheme