code: def forward(self, x):
      x1 = self.linear(x)
      x1 = self.act_layer(x1)
  
      if self.batch_norm is True:
          x1 = self.bn_layer(x1)
  
      if self.skip_connection:
          x1 = torch.cat([x, x1], axis=1)
  
      return x1
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import torch
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from sklearn.ensemble import IsolationForest
  from sklearn.preprocessing import StandardScaler, MinMaxScaler
  from torch.utils.data import DataLoader
  from .base import BaseDetector
  from ..utils.torch_utility import get_activation_by_name
label_desc: Use image node_id 2 for calling the LinearBlock obj's underlying member method code with example usage: obj.forward(x) and returns: x1