code: 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
docs: 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).