code: def forward(self, input_tensor):
      hidden_states = self.zoom_in(input_tensor)
      hidden_states = self.intermediate_act_fn(hidden_states)
      hidden_states = self.zoom_out(hidden_states)
      hidden_states = self.dropout(hidden_states)
      hidden_states = self.layerNorm(hidden_states + input_tensor)
      return hidden_states
  
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 TransformerOutput obj's underlying member method code with example usage: obj.forward(input_tensor) and returns: hidden_states