class_docstr: This corresponds to the Block class in the timm implementation.
class_docstr_tok: ["This","corresponds","to","the","Block","class","in","the","timm","implementation","."]
code: def forward(
      self,
      hidden_states,
      attention_mask=None,
      head_mask=None,
      output_attentions=False,
  ):
      self_attention_outputs = self.attention(
          self.layernorm_before(
              hidden_states
          ),  # in ViT, layernorm is applied before self-attention
          attention_mask=attention_mask,
          head_mask=head_mask,
          output_attentions=output_attentions,
      )
      attention_output = self_attention_outputs[0]
      outputs = self_attention_outputs[1:]
      # first residual connection
      hidden_states = attention_output + hidden_states
  
      # in ViT, layernorm is also applied after self-attention
      layer_output = self.layernorm_after(hidden_states)
      layer_output = self.intermediate(layer_output)
  
      # second residual connection is done here
      layer_output = self.output(layer_output, hidden_states)
      outputs = (layer_output,) + outputs
  
      return outputs
  
imports: from collections import namedtuple
  from dataclasses import asdict, dataclass
  import torch
  from mmf.utils.general import retry_n
  from omegaconf import OmegaConf
  from packaging import version
  from torch import nn
label_desc: Use image node_id 2 for calling the ViTLayer obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask, output_attentions) and returns: outputs