code: def forward(
      self,
      hidden_states,
      key_padding_mask=None,
      attention_mask=None,
      head_mask=None,
  ):
      attention_outputs = self.attention(
          hidden_states, key_padding_mask, attention_mask, head_mask
      )
      attention_output = attention_outputs[0]
      layer_output = self.output(attention_output)
      outputs = (layer_output,) + attention_outputs[1:]
      return outputs
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from .Attention import MultiHeadAttention
label_desc: Use image node_id 2 for calling the TransformerLayer obj's underlying member method code with example usage: obj.forward(hidden_states, key_padding_mask, attention_mask, head_mask) and returns: outputs