code: def trim_and_pad_dataset(dataset, length, feature_keys=None):
      """Trim and pad first dimension of features to size `length`.
  
      Args:
        dataset: tf.data.Dataset, the dataset to trimp/pad examples in.
        length: int, or a dict from feature-key to int
        feature_keys: (optional) collection of strings, the feature names to limit
          trimming/padding to. Defaults to all features.
      Returns:
        Trimmed/padded tf.data.Dataset.
      """
  
      def _trim_and_pad(k, t):
          """Trim/pad to the first axis of `t` to be of size `length`."""
          if feature_keys and k not in feature_keys:
              return t
          length_k = length if isinstance(length, int) else length[k]
          t = t[:length_k]
          pad_amt = length_k - tf.shape(t)[0]
          padded_t = tf.pad(
              t, [(0, pad_amt)] + [(0, 0)] * (len(t.shape) - 1)
          )
          padded_t.set_shape([length_k] + t.shape.as_list()[1:])
          return padded_t
  
      return dataset.map(
          lambda x: {k: _trim_and_pad(k, t) for k, t in x.items()},
          num_parallel_calls=tf.data.experimental.AUTOTUNE,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  import gin
  import tensorflow.compat.v1
  import tensorflow_datasets
label_desc: Use image node_id 17 for calling a global function with example usage: trim_and_pad_dataset(dataset, length, feature_keys) and returns: dataset, padded_t, t