code: def ResidualShuffleExchange(
      vocab_size,
      d_model,
      input_dropout,
      dropout,
      mode="train",
      n_blocks=2,
  ):
      """Returns a Residual Shuffle Exchange Network model."""
      benes_blocks = [
          BenesBlock(d_model, dropout, mode) for _ in range(n_blocks)
      ]
      return tl.Serial(
          tl.Embedding(vocab_size, d_model),
          tl.Dropout(rate=input_dropout, mode=mode),
          # Apply Benes Block n_blocks times.
          *benes_blocks,
          ResidualSwitchUnit(d_model, dropout, mode),
          # Produce probabilities.
          tl.Dense(vocab_size),
          tl.LogSoftmax(),
      )
  
imports: import numpy
  from trax import fastmath
  from trax import layers
  from trax.fastmath import numpy
  from trax.layers.assert_shape import assert_shape
label_desc: Use image node_id 11 for calling a global function with example usage: ResidualShuffleExchange(vocab_size, d_model, input_dropout, dropout, mode, n_blocks) and returns: tl