code: def _wrap_fixed_range(
      quantize_config, num_bits, init_min, init_max, narrow_range
  ):
      config = quantize_config_mod.FixedQuantizeConfig.from_config(
          {
              "config": quantize_config,
              "num_bits": num_bits,
              "init_min": init_min,
              "init_max": init_max,
              "narrow_range": narrow_range,
          }
      )
      return quantize_utils.serialize_keras_object(config)
  
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 7 for calling a global function with example usage: _wrap_fixed_range(quantize_config, num_bits, init_min, init_max, narrow_range) and returns: quantize_utils