          """
          self._var = tf.Variable(self._scale * tf.random.normal(self._input_shape,
                                                                 dtype=self.compute_dtype),
                                  trainable=True,
                                  dtype=self.compute_dtype)
          super().build(input_shape)
  
      def get_config(self) -> dict[str, T.Any]:
          """ Get the config dictionary for the layer
  
 
</s> remove                                                                dtype=self.compute_dtype),
 </s> add                                                                dtype=self.dtype), </s> remove                                  name=self._name.lower())
 </s> add                                  name="visual")
                                dtype=self.dtype)
          """
          self._var = tf.Variable(self._scale * tf.random.normal(self._input_shape,
                                                                 dtype=self.compute_dtype),
                                  trainable=True,
                                dtype=self.dtype)
          super().build(input_shape)
  
      def get_config(self) -> dict[str, T.Any]:
          """ Get the config dictionary for the layer
  
keep keep keep keep replace keep keep keep keep keep
 fixes
  - Farl weight name fix
  - Embedding layers to fp32
  - nn_blocks - unique names for conv_output
  - Phaze-A - Add clipfaker presets 
lib/model/networks/clip.py