code: def merge_output_state(
      states, X, lengths, chunk_pos_embed, hidden_dim
  ):
      if chunk_pos_embed and chunk_pos_embed.lower() == "learned":
          pos_embed = tf.compat.v1.get_variable(
              name="chunk_pos_embed",
              shape=[len(states), hidden_dim],
              initializer=tf.compat.v1.random_normal_initializer(
                  stddev=0.001
              ),
          )
          for i, state in enumerate(states):
              state["sequence_features"] = state[
                  "sequence_features"
              ] + tf.reshape(pos_embed[i], [1, 1, hidden_dim])
      else:
          assert (
              chunk_pos_embed is None
          ), "Only None and learned chunk_pos_embed are supported"
      output_state = {
          "features": tf.reduce_mean([s["features"] for s in states]),
          "sequence_features": tf.concat(
              [s["sequence_features"] for s in states], 1
          ),
          "lengths": lengths,
          "inputs": X,
      }
      if "decoder" in states[0]:
          output_state["decoder"] = states[0]["decoder"]
      if "embedding" in states[0]:
          output_state["embedding"] = states[0]["embedding"]
      if "embed_weights" in states[0]:
          output_state["embed_weights"] = states[0]["embed_weights"]
      return output_state
  
imports: import functools
  import tensorflow
  from finetune.optimizers.recompute_grads import recompute_grads_w_kwargs
label_desc: Use image node_id 1 for calling a global function with example usage: merge_output_state(states, X, lengths, chunk_pos_embed, hidden_dim) and returns: output_state