code: def quantize_annotate_layer(to_annotate, quantize_config=None):
      """Annotate a `tf.keras` layer to be quantized.
  
      This function does not actually quantize the layer. It is merely used to
      specify that the layer should be quantized. The layer then gets quantized
      accordingly when `quantize_apply` is used.
  
      This method should be used when the user wants to quantize only certain
      layers of the model, or change the default behavior of how a layer is
      quantized.
  
      Annotate a layer:
  
      ```python
      model = keras.Sequential([
          layers.Dense(10, activation='relu', input_shape=(100,)),
          quantize_annotate_layer(layers.Dense(2, activation='sigmoid'))
      ])
  
      # Only the second Dense layer is quantized.
      quantized_model = quantize_apply(model)
      ```
  
      Args:
        to_annotate: `tf.keras` layer which needs to be quantized.
        quantize_config: optional `QuantizeConfig` which controls how the layer is
          quantized. In its absence, the default behavior for the layer is used.
  
      Returns:
        `tf.keras` layer wrapped with `QuantizeAnnotate`.
      """
      if to_annotate is None:
          raise ValueError("`to_annotate` cannot be None")
  
      # Check against keras.Model since it is an instance of keras.layers.Layer.
      if not isinstance(to_annotate, keras.layers.Layer) or isinstance(
          to_annotate, keras.Model
      ):
          raise ValueError(
              "`to_annotate` can only be a `tf.keras.layers.Layer` instance. "
              "You passed an instance of type: {input}.".format(
                  input=to_annotate.__class__.__name__
              )
          )
  
      if quantize_config is not None and not isinstance(
          quantize_config, quantize_config_mod.QuantizeConfig
      ):
          raise ValueError(
              "`quantize_config` can only be a `tfmot.quantization.keras.QuantizeConfig` instance."
              "You passed an instance of type: {input}.".format(
                  input=quantize_config.__class__.__name__
              )
          )
  
      return quantize_annotate_mod.QuantizeAnnotate(
          layer=to_annotate, quantize_config=quantize_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 4 for calling a global function with example usage: quantize_annotate_layer(to_annotate, quantize_config) and returns: quantize_annotate_mod