parameters: (x, padding=((1, 1), (1, 1), (1, 1)), data_format=None)
return_statement: return array_ops.pad(x, pattern)
docstring: Pads 5D tensor with zeros along the depth, height, width dimensions.
  
    Pads these dimensions with respectively
    "padding[0]", "padding[1]" and "padding[2]" zeros left and right.
  
    For 'channels_last' data_format,
    the 2nd, 3rd and 4th dimension will be padded.
    For 'channels_first' data_format,
    the 3rd, 4th and 5th dimension will be padded.
  
    Arguments:
        x: Tensor or variable.
        padding: Tuple of 3 tuples, padding pattern.
        data_format: One of `channels_last` or `channels_first`.
  
    Returns:
        A padded 5D tensor.
  
    Raises:
        ValueError: if `data_format` is neither
            `channels_last` or `channels_first`.
docstring_summary: Pads 5D tensor with zeros along the depth, height, width dimensions.
docstring_tokens: Pads 5D tensor with zeros along the depth height width dimensions .
function: def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None):
    """Pads 5D tensor with zeros along the depth, height, width dimensions.
  
    Pads these dimensions with respectively
    "padding[0]", "padding[1]" and "padding[2]" zeros left and right.
  
    For 'channels_last' data_format,
    the 2nd, 3rd and 4th dimension will be padded.
    For 'channels_first' data_format,
    the 3rd, 4th and 5th dimension will be padded.
  
    Arguments:
        x: Tensor or variable.
        padding: Tuple of 3 tuples, padding pattern.
        data_format: One of `channels_last` or `channels_first`.
  
    Returns:
        A padded 5D tensor.
  
    Raises:
        ValueError: if `data_format` is neither
            `channels_last` or `channels_first`.
  
    """
    assert len(padding) == 3
    assert len(padding[0]) == 2
    assert len(padding[1]) == 2
    assert len(padding[2]) == 2
    if data_format is None:
      data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
      raise ValueError('Unknown data_format: ' + str(data_format))
  
    if data_format == 'channels_first':
      pattern = [[0, 0], [0, 0], [padding[0][0], padding[0][1]],
                 [padding[1][0], padding[1][1]], [padding[2][0], padding[2][1]]]
    else:
      pattern = [[0, 0], [padding[0][0], padding[0][1]],
                 [padding[1][0], padding[1][1]], [padding[2][0],
                                                  padding[2][1]], [0, 0]]
    return array_ops.pad(x, pattern)
function_tokens: def spatial_3d_padding ( x , padding = ( ( 1 , 1 ) , ( 1 , 1 ) , ( 1 , 1 ) ) , data_format = None ) : assert len ( padding ) == 3 assert len ( padding [ 0 ] ) == 2 assert len ( padding [ 1 ] ) == 2 assert len ( padding [ 2 ] ) == 2 if data_format is None : data_format = image_data_format ( ) if data_format not in { 'channels_first' , 'channels_last' } : raise ValueError ( 'Unknown data_format: ' + str ( data_format ) ) if data_format == 'channels_first' : pattern = [ [ 0 , 0 ] , [ 0 , 0 ] , [ padding [ 0 ] [ 0 ] , padding [ 0 ] [ 1 ] ] , [ padding [ 1 ] [ 0 ] , padding [ 1 ] [ 1 ] ] , [ padding [ 2 ] [ 0 ] , padding [ 2 ] [ 1 ] ] ] else : pattern = [ [ 0 , 0 ] , [ padding [ 0 ] [ 0 ] , padding [ 0 ] [ 1 ] ] , [ padding [ 1 ] [ 0 ] , padding [ 1 ] [ 1 ] ] , [ padding [ 2 ] [ 0 ] , padding [ 2 ] [ 1 ] ] , [ 0 , 0 ] ] return array_ops . pad ( x , pattern )