code: def PaddingMask(pad=0):
      """Returns a layer that maps integer sequences to padding masks.
  
      The layer expects as input a batch of integer sequences. The layer output is
      an N-D array that marks for each sequence position whether the integer (e.g.,
      a token ID) in that position represents padding -- value ``pad`` -- versus
      text/content -- all other values. The padding mask shape is
      (batch_size, 1, 1, encoder_sequence_length), such that axis 1 will broadcast
      to cover any number of attention heads and axis 2 will broadcast to cover
      decoder sequence positions.
  
      Args:
        pad: Integer that represents padding rather than a token/content ID.
      """
  
      def f(x):
          if len(x.shape) != 2:
              raise ValueError(
                  f"Input to PaddingMask must be a 2-D array with shape "
                  f"(batch_size, sequence_length); instead got shape {x.shape}."
              )
          batch_size = x.shape[0]
          sequence_length = x.shape[1]
          content_positions = x != pad
          return content_positions.reshape(
              (batch_size, 1, 1, sequence_length)
          )
  
      return Fn(f"PaddingMask({pad})", f)
  
imports: import numpy
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import convolution
  from trax.layers import core
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.base import Fn
  from trax.layers.research import sparsity
label_desc: Use image node_id 10 for calling a global function with example usage: PaddingMask(pad) and returns: Fn, content_positions