code: def __init__(
      self,
      context_length,
      target_dim,
      target_item,
      hidden_dim,
      m,
      max_norm,
      norm=True,
  ):
      super(SparseNet, self).__init__()
      self.input_dim = context_length * target_dim
      self.context_length = context_length
      self.target_dim = target_dim
      self.hidden_dim = hidden_dim
      self.m = m
      self.norm = norm
      self.gamma = nn.Parameter(
          -1.0 * torch.ones((1, self.target_dim)), requires_grad=True
      ).to(device)
      self.fc1 = nn.Linear(self.input_dim, hidden_dim).to(device)
      self.fc21 = nn.Linear(hidden_dim, self.input_dim).to(device)
      self.fc22 = nn.Linear(hidden_dim, self.input_dim).to(device)
      self.bn = nn.BatchNorm1d(hidden_dim).to(device)
      self.dist = Normal(
          torch.FloatTensor([0.0]).to(device),
          torch.FloatTensor([1.0]).to(device),
      )
      self.target_item = target_item
      self.max_norm = max_norm
  
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 1 to create a new SparseNet object from inherited base classes: nn, ABC with example: obj = SparseNet(context_length, target_dim, target_item, hidden_dim, m, max_norm, norm)