code: def fix_input_output_range(
      model,
      num_bits=8,
      input_min=0.0,
      input_max=1.0,
      output_min=0.0,
      output_max=1.0,
      narrow_range=False,
  ):
      """Fix the input and output ranges.
  
      Example:
  
      ```python
      model = keras.Sequential([
          layers.Dense(10, activation='relu', input_shape=(100,)),
          quantize_annotate_layer(layers.Dense(2, activation='sigmoid'))
      ])
      with quantize.quantize_scope():
        model = quantize_annotate_model(model)
        model = quantize_apply(model)
        model = fix_input_output_range(model, num_bits=4,
            input_min=0, input_max=15,
            output_min=0, output_max=15,
            narrow_range=False)
      ```
  
      In certain cases, a desired input/output ranges is known and should not be
      altered during training. To set these values, use the arguments as follows:
  
      Args:
        model: A `tf.keras` Sequential or Functional model which has been quantized.
        num_bits: Number of bits for quantization
        input_min: The lower end of quantization interval for the input.
        input_max: The upper end of quantization interval for the input.
        output_min: The lower end of quantization interval for the output.
        output_max: The upper end of quantization interval for the output.
        narrow_range: In case of 8 bits, narrow_range nudges the quantized range
          to be [-127, 127] instead of [-128, 127]. This ensures symmetric
          range has 0 as the centre.
  
      Returns:
        Returns a new `tf.keras` model fixed input range set to (input_min,
        input_max) and fixed output range set to (output_min, output_max).
      """
      config = model.get_config()
      fixed_input_quantizer = quantizers.FixedQuantizer(
          num_bits=num_bits,
          init_min=input_min,
          init_max=input_max,
          narrow_range=narrow_range,
      )
      serialized_fixed_input_quantizer = (
          quantize_utils.serialize_keras_object(fixed_input_quantizer)
      )
  
      if _is_functional_model(model):
          input_layer_list = _nested_to_flatten_node_data_list(
              config["input_layers"]
          )
          for layer_config in config["layers"]:
              input_name = _unwrap_first_input_name(
                  layer_config["inbound_nodes"]
              )
              if input_name is None:
                  continue
  
              for input_layer in input_layer_list:
                  if input_name == input_layer[0]:
                      layer_config["config"][
                          "quantizer"
                      ] = serialized_fixed_input_quantizer
                      break
  
          output_layer_list = _nested_to_flatten_node_data_list(
              config["output_layers"]
          )
          for layer_config in config["layers"]:
              for output_layer in output_layer_list:
                  if layer_config["config"]["name"] == output_layer[0]:
                      if "quantize_config" in layer_config["config"]:
                          layer_config["config"][
                              "quantize_config"
                          ] = _wrap_fixed_range(
                              layer_config["config"]["quantize_config"],
                              num_bits=num_bits,
                              init_min=output_min,
                              init_max=output_max,
                              narrow_range=narrow_range,
                          )
                      break
  
          model = keras.Model.from_config(config)
      else:
          if (
              len(config["layers"]) < 1
              or config["layers"][1]["class_name"] != "QuantizeLayer"
          ):
              raise ValueError("`model` should be already quantized.")
          config["layers"][1]["config"][
              "quantizer"
          ] = serialized_fixed_input_quantizer
          if "quantize_config" in config["layers"][-1]["config"]:
              config["layers"][-1]["config"][
                  "quantize_config"
              ] = _wrap_fixed_range(
                  config["layers"][-1]["config"]["quantize_config"],
                  num_bits=num_bits,
                  init_min=output_min,
                  init_max=output_max,
                  narrow_range=narrow_range,
              )
  
          model = keras.Sequential.from_config(config)
  
      return model
  
imports: import warnings
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import metrics
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_annotate
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_aware_activation
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_config
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_layer
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_wrapper
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras import utils
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_scheme
  from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantize_registry
label_desc: Use image node_id 10 for calling a global function with example usage: fix_input_output_range(model, num_bits, input_min, input_max, output_min, output_max, narrow_range) and returns: model