code: def forward(
      self,
      hidden_states,
      attention_mask,
      causal_attention_mask,
      output_attentions: bool,
      *_,
      **__,
  ):
      if output_attentions:
          raise ValueError(
              "output_attentions=True can not be supported with BetterTransformer."
          )
  
      if (
          not self.training
          and not torch.is_autocast_enabled()
          and not torch.is_autocast_cpu_enabled()
      ):
          # we expect attention_mask to be None in the vision model
          if (
              attention_mask is not None
              or causal_attention_mask is not None
          ):
              raise ValueError(
                  "Please do not use attention masks when using `BetterTransformer` converted vision models"
              )
  
          hidden_states = torch._transformer_encoder_layer_fwd(
              hidden_states,
              self.embed_dim,
              self.num_heads,
              self.in_proj_weight,
              self.in_proj_bias,
              self.out_proj_weight,
              self.out_proj_bias,
              self.use_gelu,
              self.norm_first,
              self.norm1_eps,
              self.norm1_weight,
              self.norm1_bias,
              self.norm2_weight,
              self.norm2_bias,
              self.linear1_weight,
              self.linear1_bias,
              self.linear2_weight,
              self.linear2_bias,
              attention_mask,
          )
      else:
          raise NotImplementedError(
              "Training and Autocast are not implemented for BetterTransformer + CLIP. Please open an issue."
          )
  
      return (hidden_states,)
  
imports: from typing import TYPE_CHECKING
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.activations import ACT2FN
  from .base import BetterTransformerBaseLayer
label_desc: Use image node_id 2 for calling the CLIPLayerBetterTransformer obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, causal_attention_mask, output_attentions) and returns: hidden_states