tensorflow_federated/python/learning/algorithms/fed_avg_with_optimizer_schedule.py [242:266]:
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  py_typecheck.check_callable(model_fn)

  @computations.tf_computation()
  def initial_model_weights_fn():
    return model_utils.ModelWeights.from_model(model_fn())

  model_weights_type = initial_model_weights_fn.type_signature.result

  if model_distributor is None:
    model_distributor = distributors.build_broadcast_process(model_weights_type)

  if model_aggregator is None:
    model_aggregator = mean.MeanFactory()
  py_typecheck.check_type(model_aggregator, factory.WeightedAggregationFactory)
  aggregator = model_aggregator.create(model_weights_type.trainable,
                                       computation_types.TensorType(tf.float32))
  process_signature = aggregator.next.type_signature
  input_client_value_type = process_signature.parameter[1]
  result_server_value_type = process_signature.result[1]
  if input_client_value_type.member != result_server_value_type.member:
    raise TypeError('`model_update_aggregation_factory` does not produce a '
                    'compatible `AggregationProcess`. The processes must '
                    'retain the type structure of the inputs on the '
                    f'server, but got {input_client_value_type.member} != '
                    f'{result_server_value_type.member}.')
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tensorflow_federated/python/learning/algorithms/fed_prox.py [156:180]:
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  py_typecheck.check_callable(model_fn)

  @computations.tf_computation()
  def initial_model_weights_fn():
    return model_utils.ModelWeights.from_model(model_fn())

  model_weights_type = initial_model_weights_fn.type_signature.result

  if model_distributor is None:
    model_distributor = distributors.build_broadcast_process(model_weights_type)

  if model_aggregator is None:
    model_aggregator = mean.MeanFactory()
  py_typecheck.check_type(model_aggregator, factory.WeightedAggregationFactory)
  aggregator = model_aggregator.create(model_weights_type.trainable,
                                       computation_types.TensorType(tf.float32))
  process_signature = aggregator.next.type_signature
  input_client_value_type = process_signature.parameter[1]
  result_server_value_type = process_signature.result[1]
  if input_client_value_type.member != result_server_value_type.member:
    raise TypeError('`model_update_aggregation_factory` does not produce a '
                    'compatible `AggregationProcess`. The processes must '
                    'retain the type structure of the inputs on the '
                    f'server, but got {input_client_value_type.member} != '
                    f'{result_server_value_type.member}.')
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