(dataset,
                            training,
                            len_map=None,
                            truncate_on_eval=False)
return dataset.map(truncate_example)
Truncates features in an example to lengths given in `len_map`.

  Args:
    dataset: `tf.data.Dataset` the dataset to filter.
    training: bool, true if we are in training mode.
    len_map: optional dict of str to int, we truncate examples where a feature's
      size is beyond the max. Ex: {'inputs': 512, 'targets': 64} will truncate
        examples to be within those bounds.
    truncate_on_eval: bool if true, we will truncate in eval mode also.

  Returns:
    a filtered `tf.data.Dataset`.
Truncates features in an example to lengths given in `len_map`.
Truncates features in an example to lengths given in len_map .
def truncate_dataset_on_len(dataset,
                            training,
                            len_map=None,
                            truncate_on_eval=False):
  """Truncates features in an example to lengths given in `len_map`.

  Args:
    dataset: `tf.data.Dataset` the dataset to filter.
    training: bool, true if we are in training mode.
    len_map: optional dict of str to int, we truncate examples where a feature's
      size is beyond the max. Ex: {'inputs': 512, 'targets': 64} will truncate
        examples to be within those bounds.
    truncate_on_eval: bool if true, we will truncate in eval mode also.

  Returns:
    a filtered `tf.data.Dataset`.
  """
  if (len_map is None) or (not training and not truncate_on_eval):
    return dataset

  assert isinstance(len_map, dict)

  def truncate_example(x):
    for key, max_len in len_map.items():
      x_len = tf.shape(x[key])[0]
      if x_len > max_len:
        x[key] = x[key][:max_len, ...]
    return x

  return dataset.map(truncate_example)
def truncate_dataset_on_len ( dataset , training , len_map = None , truncate_on_eval = False ) : if ( len_map is None ) or ( not training and not truncate_on_eval ) : return dataset assert isinstance ( len_map , dict ) def truncate_example ( x ) : for key , max_len in len_map . items ( ) : x_len = tf . shape ( x [ key ] ) [ 0 ] if x_len > max_len : x [ key ] = x [ key ] [ : max_len , ... ] return x return dataset . map ( truncate_example )