code: def forward(self, x, n_sample=100):  # x is batch_size by input_dim
      x = x.view(x.shape[0], self.input_dim)
      x = self.fc1(x)
      x_reshape = x.view(-1, self.fc1.out_features)
      if (x_reshape.shape[0] > 1) and (self.norm is True):
          shape = x.shape
          x = x.view(-1, self.fc1.out_features)
          x = F.relu(self.bn(x)).view(shape)
      else:
          x = F.relu(x)
      mu, log_var = self.fc21(x), self.fc22(x)
      std = torch.exp(0.5 * log_var)
      eps = (
          torch.empty(n_sample, mu.shape[0], self.input_dim)
          .normal_(0, 1)
          .to(device)
      )
      w = mu + eps * std
      w = w.mean(0)
      u = (
          torch.empty(mu.shape[0], self.target_dim)
          .normal_(0, 1)
          .to(device)
      )
      r = self._r()
      r = r.repeat(mu.shape[0], 1)
      mask = (u <= self.dist.icdf(r)).to(device)  # batch x target_dim
      mask_target_item = torch.ones(mu.shape[0], self.target_dim).to(
          device
      )
      if self.target_item is not None:
          mask_target_item[:, self.target_item] = 0
  
      # renorm output
      output = w.view(-1, self.context_length, self.target_dim) * (
          mask.float() * mask_target_item
      ).reshape(-1, 1, self.target_dim).repeat(
          1, self.context_length, 1
      )  # batch x context x target dim
      output.clamp_(min=-self.max_norm, max=self.max_norm)
      return output.view(-1, self.context_length, self.target_dim)
  
file_path: gluonts/src/gluonts/nursery/robust-mts-attack/sparse_layer.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.distributions
  from torch.distributions import Normal
  from abc import ABC
  import numpy
  import warnings
  import os, pickle
label_desc: Use image node_id 3 for calling the SparseNet obj's underlying member method code with example usage: obj.forward(x, n_sample) and returns: output