code: def forward(self, num_parallel_samples=100):
      perturbed_inputs = dict(
          [(key, self.batch[key]) for key in self.input_names]
      )
      perturbed_inputs["past_target_cdf"] = self.batch[
          "past_target_cdf"
      ] * (1 + self.perturbation)
      _, samples = forward_model(self.model, perturbed_inputs)
      return samples
  
imports: import torch
  import torch.nn
  import torch.optim
  from tqdm.auto import tqdm
  from typing import List
  from sparse_layer import SparseNet
label_desc: Use image node_id 2 for calling the AttackModule obj's underlying member method code with example usage: obj.forward(num_parallel_samples) and returns: samples