code: def EncoderDecoderMask():
      """Returns a layer that creates a mask for encoder-decoder cross attention.
  
      The layer expects two inputs:
  
          - decoder_input: batch of integer (e.g., token ID) sequences
          - mask: padding mask from the encoder
  
      The layer output is a mask that marks for each sequence position (for both
      encoder and decoder) whether that position can be attended to or not. The
      encoder-decoder mask shape is (batch_size, 1, decoder_sequence_length,
      encoder_sequence_length), such that axis 1 will automatically broadcast to
      cover any number of attention heads.
      """
  
      def f(decoder_input, mask):
          if len(decoder_input.shape) != 3:
              raise ValueError(
                  f"Decoder input to EncoderDecoderMask must be a 3-D array with "
                  f"shape (batch_size, decoder_sequence_length, d_model); instead got "
                  f"shape {decoder_input.shape}."
              )
          batch_size = mask.shape[0]
          encoder_sequence_length = mask.shape[-1]
          decoder_sequence_length = decoder_input.shape[1]
          mask = mask.reshape(
              (batch_size, 1, 1, encoder_sequence_length)
          )
          return mask + jnp.zeros((1, 1, decoder_sequence_length, 1))
  
      return Fn("EncoderDecoderMask", 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 11 for calling a global function with example usage: EncoderDecoderMask() and returns: Fn, unknown