class_docstr: Output layer that incrementally adds units whenever new classes are
  encountered.
  
  Typically used in class-incremental benchmarks where the number of
  classes grows over time.
class_docstr_tok: ["Output","layer","that","incrementally","adds","units","whenever","new","classes","are","encountered",".","Typically","used","in","class-incremental","benchmarks","where","the","number","of","classes","grows","over","time","."]
docstr: compute the output given the input `x`. This module does not use
  the task label.
  
  :param x:
  :return:
docstr_tok: ["compute","the","output","given","the","input","`","x","`",".","This","module","does","not","use","the","task","label",".",":","param","x",":",":","return",":"]
code: def forward(self, x, **kwargs):
      """compute the output given the input `x`. This module does not use
      the task label.
  
      :param x:
      :return:
      """
      out = self.classifier(x)
      if self.masking:
          mask = torch.logical_not(self.active_units)
          out = out.masked_fill(mask=mask, value=self.mask_value)
      return out
  
imports: import torch
  from torch.nn import Module
  from typing import Optional
  from avalanche.benchmarks.utils.flat_data import ConstantSequence
  from avalanche.benchmarks.scenarios import CLExperience
label_desc: Use image node_id 3 for calling the IncrementalClassifier obj's underlying member method code with example usage: obj.forward(x) and returns: out