def fixed_padding(inputs, kernel_size, data_format="channels_first"):
  """Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: `Tensor` of size `[batch, channels, height, width]` or
        `[batch, height, width, channels]` depending on `data_format`.
    kernel_size: `int` kernel size to be used for `conv2d` or max_pool2d`
        operations. Should be a positive integer.
    data_format: `str` either "channels_first" for `[batch, channels, height,
        width]` or "channels_last for `[batch, height, width, channels]`.

  Returns:
    A padded `Tensor` of the same `data_format` with size 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
Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: `Tensor` of size `[batch, channels, height, width]` or
        `[batch, height, width, channels]` depending on `data_format`.
    kernel_size: `int` kernel size to be used for `conv2d` or max_pool2d`
        operations. Should be a positive integer.
    data_format: `str` either "channels_first" for `[batch, channels, height,
        width]` or "channels_last for `[batch, height, width, channels]`.

  Returns:
    A padded `Tensor` of the same `data_format` with size either intact
    (if `kernel_size == 1`) or padded (if `kernel_size > 1`).
  
tensor2tensor/models/resnet.py