code: def remove_input_range(model):
      """Remove the input range.
  
      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 = remove_input_range(model)
      ```
  
      In certain cases, a desired input range is not required if the model itself is
      internally used.
  
      Args:
        model: A `tf.keras` Sequential or Functional model which has been quantized.
  
      Returns:
        Returns a new `tf.keras` model removed input range.
      """
      config = model.get_config()
      no_input_quantizer = quantizers.NoQuantizer()
      serialized_input_quantizer = (
          quantize_utils.serialize_keras_object(no_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_input_quantizer
                      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_input_quantizer
  
          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 12 for calling a global function with example usage: remove_input_range(model) and returns: model