code: def Interleave():
      """Layer that interleaves and flattens two serialized sequences.
  
      The first sequence can be longer by 1 than the second one. This is so we can
      interleave sequences of observations and actions, when there's 1 extra
      observation at the end.
  
      For serialized sequences [[x_1_1, ..., x_1_R1], ..., [x_L1_1, ..., x_L1_R1]]
      and [[y_1_1, ..., y_1_R2], ..., [y_L2_1, ..., y_L2_R2]], where L1 = L2 + 1,
      the result is [x_1_1, ..., x_1_R1, y_1_1, ..., y_1_R2, ..., x_L2_1, ...,
      x_L2_R1, y_L2_1, ..., y_L2_R2, x_L1_1, ..., x_L1_R1] (batch dimension omitted
      for clarity).
  
      The layer inputs are a sequence pair of shapes (B, L1, R1) and (B, L2, R2),
      where B is batch size, L* is the length of the sequence and R* is the
      representation length of each element in the sequence.
  
      Returns:
        Layer that interleaves sequence of shape (B, L1 * R1 + L2 * R2).
      """
  
      def interleave(x, y):
          (batch_size, _, _) = x.shape
          (_, length, _) = y.shape
          assert x.shape[1] in (length, length + 1)
  
          reprs = jnp.concatenate((x[:, :length], y), axis=2)
          reprs = jnp.reshape(reprs, (batch_size, -1))
          remainder = jnp.reshape(x[:, length:], (batch_size, -1))
          return jnp.concatenate((reprs, remainder), axis=1)
  
      return tl.Fn("Interleave", interleave)
  
imports: import functools
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
  from trax.rl import space_serializer
label_desc: Use image node_id 2 for calling a global function with example usage: Interleave() and returns: tl, jnp