code: def forward(self, x):
      x0 = self.linear0(x[0])
      x1 = self.linear1(x[1])
  
      if self.activ_input:
          x0 = getattr(F, self.activ_input)(x0)
          x1 = getattr(F, self.activ_input)(x1)
  
      if self.dropout_input > 0:
          x0 = F.dropout(
              x0, p=self.dropout_input, training=self.training
          )
          x1 = F.dropout(
              x1, p=self.dropout_input, training=self.training
          )
  
      z = x0 * x1
  
      if self.dropout_pre_norm > 0:
          z = F.dropout(
              z, p=self.dropout_pre_norm, training=self.training
          )
  
      z = z.view(z.size(0), self.mm_dim, self.factor)
      z = z.sum(2)
  
      if self.normalize:
          z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z))
          z = F.normalize(z, p=2)
  
      z = self.linear_out(z)
  
      if self.activ_output:
          z = getattr(F, self.activ_output)(z)
  
      if self.dropout_output > 0:
          z = F.dropout(
              z, p=self.dropout_output, training=self.training
          )
      return z
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmf.common.registry import registry
  from mmf.utils.general import get_chunks, get_sizes_list, irfft, rfft
  from mmf.utils.logger import log_class_usage
label_desc: Use image node_id 2 for calling the MFB obj's underlying member method code with example usage: obj.forward(x) and returns: z