code: 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
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 3 for calling a global function with example usage: fixed_padding(inputs, kernel_size, data_format) and returns: padded_inputs