parameters: (self, data)
docstring: Batchify the input data.
  
          The input can be list of numpy.ndarray, list of numbers or list of
          mxnet.nd.NDArray. Inputting mxnet.nd.NDArray is discouraged as each
          array need to be converted to numpy for efficient padding.
          The arrays will be padded to the largest dimension at `axis` and then
          stacked to form the final output.
  
          Parameters
          ----------
          data : List[np.ndarray] or List[List[dtype]] or List[nd.NDArray]
              List of samples to pad and stack.
          Returns
          -------
          batch_data: NDArray
              Data in the minibatch. Shape is (N, ...)
docstring_summary: Batchify the input data.
docstring_tokens: Batchify the input data .
function: def __call__(self, data):
          """Batchify the input data.
  
          The input can be list of numpy.ndarray, list of numbers or list of
          mxnet.nd.NDArray. Inputting mxnet.nd.NDArray is discouraged as each
          array need to be converted to numpy for efficient padding.
          The arrays will be padded to the largest dimension at `axis` and then
          stacked to form the final output.
  
          Parameters
          ----------
          data : List[np.ndarray] or List[List[dtype]] or List[nd.NDArray]
              List of samples to pad and stack.
          Returns
          -------
          batch_data: NDArray
              Data in the minibatch. Shape is (N, ...)
          """
          _arr = _np if is_np_array() else nd
          _arr_cls = _arr.ndarray if is_np_array() else _arr.NDArray
          if isinstance(data[0], _arr_cls) and not self._warned:
              self._warned = True
              warnings.warn(
                  'Using Pad with NDArrays is discouraged for speed reasons. '
                  'Instead you should pad your data while it is still a list '
                  'and before converting to an NDArray. '
                  'Alternatively you can consider inputting a numpy.ndarray.')
          if isinstance(data[0], (_arr_cls, np.ndarray, list)):
              padded_arr = _pad_arrs_to_max_length(data, self._pad_val,
                                                   self._use_shared_mem,
                                                   self._dtype, self._round_to)
              return padded_arr
          else:
              raise NotImplementedError(
                  "Pad() does not support multiple items, use Group(Pad(), Pad(), ...) instead")
function_tokens: def __call__ ( self , data ) : _arr = _np if is_np_array ( ) else nd _arr_cls = _arr . ndarray if is_np_array ( ) else _arr . NDArray if isinstance ( data [ 0 ] , _arr_cls ) and not self . _warned : self . _warned = True warnings . warn ( 'Using Pad with NDArrays is discouraged for speed reasons. ' 'Instead you should pad your data while it is still a list ' 'and before converting to an NDArray. ' 'Alternatively you can consider inputting a numpy.ndarray.' ) if isinstance ( data [ 0 ] , ( _arr_cls , np . ndarray , list ) ) : padded_arr = _pad_arrs_to_max_length ( data , self . _pad_val , self . _use_shared_mem , self . _dtype , self . _round_to ) return padded_arr else : raise NotImplementedError ( "Pad() does not support multiple items, use Group(Pad(), Pad(), ...) instead" )