code: def deserialize_keras_model(header, frames):
      from keras.models import model_from_config
  
      n = 0
      weights = []
      for head, length in zip(header["headers"], header["nframes"]):
          x = deserialize(head, frames[n : n + length])
          weights.append(x)
          n += length
      model = model_from_config(header)
      model.set_weights(weights)
      return model
  
imports: from __future__ import annotations
  import keras
  from distributed.protocol.serialize import dask_deserialize, dask_serialize, deserialize, serialize
label_desc: Use image node_id 2 for calling a global function with example usage: deserialize_keras_model(header, frames) and returns: model