(inputs, kernel_size, data_format)
return padded_inputs
Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or
      [batch, height_in, width_in, channels] depending on data_format.
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
                 Should be a positive integer.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    A tensor with the same format as the input with the data either intact
    (if kernel_size == 1) or padded (if kernel_size > 1).
Pads the input along the spatial dimensions independently of input size.
Pads the input along the spatial dimensions independently of input size .
def fixed_padding(inputs, kernel_size, data_format):
  """Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or
      [batch, height_in, width_in, channels] depending on data_format.
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
                 Should be a positive integer.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    A tensor with the same format as the input with the data either intact
    (if kernel_size == 1) or padded (if kernel_size > 1).
  """
  pad_total = kernel_size - 1
  pad_beg = pad_total // 2
  pad_end = pad_total - pad_beg

  if data_format == 'channels_first':
    padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],
                                    [pad_beg, pad_end], [pad_beg, pad_end]])
  else:
    padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
                                    [pad_beg, pad_end], [0, 0]])
  return padded_inputs
def fixed_padding ( inputs , kernel_size , data_format ) : pad_total = kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg if data_format == 'channels_first' : padded_inputs = tf . pad ( inputs , [ [ 0 , 0 ] , [ 0 , 0 ] , [ pad_beg , pad_end ] , [ pad_beg , pad_end ] ] ) else : padded_inputs = tf . pad ( inputs , [ [ 0 , 0 ] , [ pad_beg , pad_end ] , [ pad_beg , pad_end ] , [ 0 , 0 ] ] ) return padded_inputs