code: def forward_wide(self, x):
      # x has shape (batch_size, input_width)
      batch_size = x.shape[0]
  
      # compute the mixing coefficients
      prod = torch.matmul(x, self.mixer)  # (batch_size, n_blocks)
      h_x = prod  # (batch_size, n_blocks)
      scores = torch.softmax(h_x, dim=-1)  # (batch_size, n_blocks)
  
      # retrieve the relevant parameters
      w1s = self.w1s.unsqueeze(
          0
      )  # (1, n_blocks, input_width, block_size)
      b1s = self.b1s.unsqueeze(0)  # (1, n_blocks, block_size)
      w2s = self.w2s.unsqueeze(
          0
      )  # (1, n_blocks, block_size, output_width)
      b2s = self.b2s.unsqueeze(0)  # (1, n_blocks, output_width)
  
      # compute the forward pass
      val = torch.matmul(
          x.unsqueeze(1).unsqueeze(1), w1s
      ) + b1s.unsqueeze(
          -2
      )  # (batch_size, n_blocks, 1, block_size)
      val = torch.nn.functional.relu(
          val
      )  # (batch_size, n_blocks, 1, block_size)
      val = (
          torch.matmul(val, w2s).squeeze(-2) + b2s
      )  # (batch_size, n_blocks, output_width)
  
      mixed_result = torch.sum(
          val * scores.unsqueeze(-1), dim=1
      )  # (batch_size, output_width)
  
      return mixed_result
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the MoENetwork obj's underlying member method code with example usage: obj.forward_wide(x) and returns: mixed_result