parameters: (self, batch_size, padded_shapes, padding_values=None)
return_statement: return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values)
docstring: Combines consecutive elements of this dataset into padded batches.
  
      Like `Dataset.dense_to_sparse_batch()`, this method combines
      multiple consecutive elements of this dataset, which might have
      different shapes, into a single element. The tensors in the
      resulting element have an additional outer dimension, and are
      padded to the respective shape in `padded_shapes`.
  
      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 representing the shape
          to which the respective component of each input element should
          be padded prior to batching. Any unknown dimensions
          (e.g. `tf.Dimension(None)` in a `tf.TensorShape` or `-1` in a
          tensor-like object) will be padded to the maximum size of that
          dimension in each batch.
        padding_values: (Optional.) A nested structure of scalar-shaped
          `tf.Tensor`, representing the padding values to use for the
          respective components.  Defaults are `0` for numeric types and
          the empty string for string types.
  
      Returns:
        A `Dataset`.
docstring_summary: Combines consecutive elements of this dataset into padded batches.
docstring_tokens: Combines consecutive elements of this dataset into padded batches .
function: def padded_batch(self, batch_size, padded_shapes, padding_values=None):
      """Combines consecutive elements of this dataset into padded batches.
  
      Like `Dataset.dense_to_sparse_batch()`, this method combines
      multiple consecutive elements of this dataset, which might have
      different shapes, into a single element. The tensors in the
      resulting element have an additional outer dimension, and are
      padded to the respective shape in `padded_shapes`.
  
      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 representing the shape
          to which the respective component of each input element should
          be padded prior to batching. Any unknown dimensions
          (e.g. `tf.Dimension(None)` in a `tf.TensorShape` or `-1` in a
          tensor-like object) will be padded to the maximum size of that
          dimension in each batch.
        padding_values: (Optional.) A nested structure of scalar-shaped
          `tf.Tensor`, representing the padding values to use for the
          respective components.  Defaults are `0` for numeric types and
          the empty string for string types.
  
      Returns:
        A `Dataset`.
      """
      return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values)
function_tokens: def padded_batch ( self , batch_size , padded_shapes , padding_values = None ) : return PaddedBatchDataset ( self , batch_size , padded_shapes , padding_values )