          if self._load_weights:
              model_path = GetModel(f"CLIPv_{self._name}_v1.h5", self._git_id).model_path
              net.load_weights(model_path, by_name=True, skip_mismatch=True)
  
          return net
  
 
</s> remove                                   name=self._name.lower())
 </s> add                                   name="visual") </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                  *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)
            logger.info("Loading CLIPv trained weights for '%s'", self._name)
          if self._load_weights:
              model_path = GetModel(f"CLIPv_{self._name}_v1.h5", self._git_id).model_path
            logger.info("Loading CLIPv trained weights for '%s'", self._name)
              net.load_weights(model_path, by_name=True, skip_mismatch=True)
  
          return net
  
keep add 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