class_docstr: Equivalent to IncrementalClassifier but using the cosine layer
  described in "Learning a Unified Classifier Incrementally via Rebalancing"
  by Saihui Hou et al.
class_docstr_tok: ["Equivalent","to","IncrementalClassifier","but","using","the","cosine","layer","described","in","``","Learning","a","Unified","Classifier","Incrementally","via","Rebalancing","''","by","Saihui","Hou","et","al","."]
code: def _generate_fc(self, in_dim, out_dim):
      fc = SplitCosineLinear(
          in_dim, self.fc.out_features, out_dim - self.fc.out_features
      )
      return fc
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from avalanche.models import DynamicModule
label_desc: Use image node_id 4 for calling the CosineIncrementalClassifier obj's underlying member method code with example usage: obj._generate_fc(in_dim, out_dim) and returns: fc