code: def forward(
      self,
      input_tensor1,
      attention_mask1,
      input_tensor2,
      attention_mask2,
      co_attention_mask=None,
  ):
      bi_output1, bi_output2 = self.bimodal_attention(
          input_tensor1,
          input_tensor2,
          attention_mask1,
          attention_mask2,
          co_attention_mask,
      )
  
      attention_output1, attention_output2 = self.bimodal_output(
          bi_output2, input_tensor1, bi_output1, input_tensor2
      )
  
      intermediate_output1 = self.intermediate1(attention_output1)
      layer_output1 = self.output1(
          intermediate_output1, attention_output1
      )
  
      intermediate_output2 = self.intermediate2(attention_output2)
      layer_output2 = self.output2(
          intermediate_output2, attention_output2
      )
  
      return layer_output1, layer_output2
  
file_path: allennlp/allennlp/modules/transformer/bimodal_connection_layer.py
imports: from allennlp.common import FromParams
  from allennlp.modules.transformer.activation_layer import ActivationLayer
  from allennlp.modules.transformer.output_layer import OutputLayer
  from allennlp.modules.transformer.bimodal_attention import BiModalAttention
  from allennlp.modules.transformer.transformer_module import TransformerModule
label_desc: Use image node_id 2 for calling the BiModalConnectionLayer obj's underlying member method code with example usage: obj.forward(input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask) and returns: layer_output1,  layer_output2