code: def forward(self, hidden_states, 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()
      ):
          attention_mask = None
  
          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,
          )
          if hidden_states.is_nested and self.is_last_layer:
              hidden_states = hidden_states.to_padded_tensor(0.0)
      else:
          raise NotImplementedError(
              "Training and Autocast are not implemented for BetterTransformer + ViT. 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 ViTLayerBetterTransformer obj's underlying member method code with example usage: obj.forward(hidden_states, output_attentions) and returns: hidden_states