code: def forward(self, x):
      # x has shape (batch_size, input_width)
      batch_size = x.shape[0]
  
      # compute the mixing coefficients
      mixer_prod = torch.matmul(x, self.mixer)
      noise_prod = torch.matmul(x, self.noise_controller)
      h_x = mixer_prod + torch.randn(
          mixer_prod.shape, device=x.device
      ) * torch.nn.functional.softplus(
          noise_prod
      )  # (batch_size, n_blocks)
      h_x_topk_values, h_x_topk_indices = torch.topk(
          h_x, self.k, dim=-1
      )  # (batch_size, k), (batch_size, k)
      scores = torch.softmax(h_x_topk_values, dim=-1)  # (batch_size, k)
  
      # retrieve the relevant parameters
      w1s = self.w1s.index_select(
          dim=0, index=h_x_topk_indices.flatten()
      ).reshape(
          *h_x_topk_indices.shape, self.w1s.shape[1], self.w1s.shape[2]
      )  # (batch_size, k, input_width, block_size)
      b1s = self.b1s.index_select(
          dim=0, index=h_x_topk_indices.flatten()
      ).reshape(
          *h_x_topk_indices.shape, self.b1s.shape[1]
      )  # (batch_size, k, block_size)
      w2s = self.w2s.index_select(
          dim=0, index=h_x_topk_indices.flatten()
      ).reshape(
          *h_x_topk_indices.shape, self.w2s.shape[1], self.w2s.shape[2]
      )  # (batch_size, k, block_size, output_width)
      b2s = self.b2s.index_select(
          dim=0, index=h_x_topk_indices.flatten()
      ).reshape(
          *h_x_topk_indices.shape, self.b2s.shape[1]
      )  # (batch_size, k, output_width)
  
      # compute the forward pass
      val = torch.matmul(
          x.unsqueeze(1).unsqueeze(1), w1s
      ) + b1s.unsqueeze(
          -2
      )  # (batch_size, k, 1, block_size)
      val = torch.nn.functional.relu(
          val
      )  # (batch_size, k, 1, block_size)
      val = (
          torch.matmul(val, w2s).squeeze(-2) + b2s
      )  # (batch_size, k, 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 3 for calling the MoENetwork obj's underlying member method code with example usage: obj.forward(x) and returns: mixed_result