parameters: (batch_size,
                                      padded_shapes,
                                      padding_values=None)
return_statement: return _apply_fn
docstring: A batching and padding transformation that omits the final small batch.
  
    Like `tf.data.Dataset.padded_batch`, this transformation combines
    consecutive elements of this dataset into batches. However, if the batch
    size does not evenly divide the input dataset size, this transformation will
    drop the final smaller element.
  
    See `tf.contrib.data.batch_and_drop_remainder` for more details.
  
    Args:
      batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
        consecutive elements of this dataset to combine in a single batch.
      padded_shapes: A nested structure of `tf.TensorShape` or `tf.int64` vector
        tensor-like objects. See `tf.data.Dataset.padded_batch` for details.
      padding_values: (Optional.) A nested structure of scalar-shaped `tf.Tensor`.
        See `tf.data.Dataset.padded_batch` for details.
  
    Returns:
      A `Dataset` transformation function, which can be passed to
      `tf.data.Dataset.apply`
docstring_summary: A batching and padding transformation that omits the final small batch.
docstring_tokens: A batching and padding transformation that omits the final small batch .
function: def padded_batch_and_drop_remainder(batch_size,
                                      padded_shapes,
                                      padding_values=None):
    """A batching and padding transformation that omits the final small batch.
  
    Like `tf.data.Dataset.padded_batch`, this transformation combines
    consecutive elements of this dataset into batches. However, if the batch
    size does not evenly divide the input dataset size, this transformation will
    drop the final smaller element.
  
    See `tf.contrib.data.batch_and_drop_remainder` for more details.
  
    Args:
      batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
        consecutive elements of this dataset to combine in a single batch.
      padded_shapes: A nested structure of `tf.TensorShape` or `tf.int64` vector
        tensor-like objects. See `tf.data.Dataset.padded_batch` for details.
      padding_values: (Optional.) A nested structure of scalar-shaped `tf.Tensor`.
        See `tf.data.Dataset.padded_batch` for details.
  
    Returns:
      A `Dataset` transformation function, which can be passed to
      `tf.data.Dataset.apply`
    """
  
    def _apply_fn(dataset):
      """Function from `Dataset` to `Dataset` that applies the transformation."""
      return dataset.padded_batch(
          batch_size, padded_shapes=padded_shapes, padding_values=padding_values,
          drop_remainder=True)
  
    return _apply_fn
function_tokens: def padded_batch_and_drop_remainder ( batch_size , padded_shapes , padding_values = None ) : def _apply_fn ( dataset ) : """Function from `Dataset` to `Dataset` that applies the transformation.""" return dataset . padded_batch ( batch_size , padded_shapes = padded_shapes , padding_values = padding_values , drop_remainder = True ) return _apply_fn