code: def forward(self, x):
      x0 = self.linear0(x[0])
      x1 = self.linear1(x[1])
      bsize = x1.size(0)
      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
          )
      x0_chunks = get_chunks(x0, self.sizes_list)
      x1_chunks = get_chunks(x1, self.sizes_list)
      zs = []
      for chunk_id, m0, m1 in zip(
          range(len(self.sizes_list)),
          self.merge_linears0,
          self.merge_linears1,
      ):
          x0_c = x0_chunks[chunk_id]
          x1_c = x1_chunks[chunk_id]
          m = m0(x0_c) * m1(x1_c)  # bsize x split_size*rank
          m = m.view(bsize, self.rank, -1)
          z = torch.sum(m, 1)
          if self.pos_norm == "before_cat":
              z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z))
              z = F.normalize(z, p=2)
          zs.append(z)
      z = torch.cat(zs, 1)
      if self.pos_norm == "after_cat":
          z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z))
          z = F.normalize(z, p=2)
  
      if self.dropout_pre_lin > 0:
          z = F.dropout(
              z, p=self.dropout_pre_lin, training=self.training
          )
      z = self.linear_out(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 Block obj's underlying member method code with example usage: obj.forward(x) and returns: z