code: def provide_batch(
      examples,
      preprocess_examples,
      params,
      is_training,
      shuffle_examples,
      skip_n_initial_records,
  ):
      """Returns batches of tensors read from TFRecord files.
  
      Args:
        examples: A string path to a TFRecord file of examples, a python list of
          serialized examples, or a Tensor placeholder for serialized examples.
        preprocess_examples: Whether to preprocess examples. If False, assume they
          have already been preprocessed.
        params: HParams object specifying hyperparameters. Called 'params' here
          because that is the interface that TPUEstimator expects.
        is_training: Whether this is a training run.
        shuffle_examples: Whether examples should be shuffled.
        skip_n_initial_records: Skip this many records at first.
  
      Returns:
        Batched tensors in a TranscriptionData NamedTuple.
      """
      hparams = params
  
      input_dataset = read_examples(
          examples,
          is_training,
          shuffle_examples,
          skip_n_initial_records,
          hparams,
      )
  
      if preprocess_examples:
          input_map_fn = functools.partial(
              preprocess_example,
              hparams=hparams,
              is_training=is_training,
          )
      else:
          input_map_fn = parse_preprocessed_example
      input_tensors = input_dataset.map(input_map_fn)
  
      model_input = input_tensors.map(
          functools.partial(
              input_tensors_to_model_input,
              hparams=hparams,
              is_training=is_training,
          )
      )
  
      model_input = splice_examples(model_input, hparams, is_training)
      dataset = create_batch(
          model_input, hparams=hparams, is_training=is_training
      )
      return dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
  
imports: import collections
  import functools
  import io
  import re
  import wave
  import zlib
  import librosa
  from magenta.models.onsets_frames_transcription import audio_transform
  from magenta.models.onsets_frames_transcription import constants
  from magenta.models.onsets_frames_transcription import drum_mappings
  from magenta.models.onsets_frames_transcription import melspec_input
  from note_seq import audio_io
  from note_seq import sequences_lib
  from note_seq.protobuf import music_pb2
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 29 for calling a global function with example usage: provide_batch(examples, preprocess_examples, params, is_training, shuffle_examples, skip_n_initial_records) and returns: dataset