      scale: int
          Amount to scale the random initialization by
      name: str
          The name of the layer
      """
      def __init__(self,
                   input_shape: tuple[int, ...],
                   scale: int,
                   name: str,
                   *args,
 
</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                                                                dtype=self.compute_dtype),
 </s> add                                                                dtype=self.dtype), </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( </s> remove                                 dtype=self.compute_dtype)
 </s> add                                 dtype=self.dtype)
    dtype: str, optional
        The datatype for the layer. Mixed precision can mess up the embeddings. Default: "float32"
      scale: int
          Amount to scale the random initialization by
      name: str
          The name of the layer
    dtype: str, optional
        The datatype for the layer. Mixed precision can mess up the embeddings. Default: "float32"
      """
      def __init__(self,
                   input_shape: tuple[int, ...],
                   scale: int,
                   name: str,
                   *args,
keep keep keep add keep 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