code: def __init__(
      self,
      num_nodes=50,
      ip_dim=1,
      op_dim=1,
      activation_type="relu",
      args=None,
  ):
      super(FCNet, self).__init__()
      self.args = args
      if activation_type == "relu":
          self.activation = nn.ReLU()
      elif activation_type == "tanh":
          self.activation = nn.Tanh()
      else:
          print("Activation Type not supported")
          return
      layer = Linear
      self.fc_hidden = []
      self.fc1 = layer(ip_dim, num_nodes)
      self.bn1 = nn.BatchNorm1d(num_nodes)
      for _ in np.arange(self.args.num_layers - 1):
          self.fc_hidden.append(layer(num_nodes, num_nodes))
          self.fc_hidden.append(nn.BatchNorm1d(num_nodes))
          self.fc_hidden.append(self.activation)
      self.features = nn.Sequential(*self.fc_hidden)
      self.fc_out = layer(num_nodes, op_dim)
  
imports: import matplotlib
  import matplotlib.pyplot
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  from torch.nn import Linear
  import os
label_desc: Use image node_id 1 to create a new FCNet object from inherited base classes: nn with example: obj = FCNet(num_nodes, ip_dim, op_dim, activation_type, args)