          input_shape: tuple[int, ...
              The input shape of the incoming tensor
          """
          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]:
 
</s> remove                                 dtype=self.compute_dtype)
 </s> add                                 dtype=self.dtype) </s> remove                  *args, **kwargs) -> None:
        super().__init__(name=name, *args, **kwargs)
 </s> add                  *args,
                 dtype="float32",
                 **kwargs) -> None:
        super().__init__(name=name, dtype=dtype, *args, **kwargs) </s> remove         self._name = kwargs.pop("name") if "name" in kwargs else _get_name(
 </s> add         self._name = _get_name(kwargs.pop("name")) if "name" in kwargs else _get_name(
                                                               dtype=self.dtype),
          input_shape: tuple[int, ...
              The input shape of the incoming tensor
          """
          self._var = tf.Variable(self._scale * tf.random.normal(self._input_shape,
                                                               dtype=self.dtype),
                                  trainable=True,
                                  dtype=self.compute_dtype)
          super().build(input_shape)
  
      def get_config(self) -> dict[str, T.Any]:
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