class_docstr: Regular and domain classifier network for regular-size images
  
  Args:
      input_dim (int, optional): the dimension of the final feature vector.. Defaults to 15.
      n_classes (int, optional): the number of classes. Defaults to 2.
      name (str, optional): the classifier name. Defaults to "c".
      hidden (tuple, optional): the hidden layer sizes. Defaults to ().
      activation_fn ([type], optional): the activation function. Defaults to nn.ReLU.
class_docstr_tok: ["Regular","and","domain","classifier","network","for","regular-size","images","Args",":","input_dim","(","int",",","optional",")",":","the","dimension","of","the","final","feature","vector","..","Defaults","to","15.","n_classes","(","int",",","optional",")",":","the","number","of","classes",".","Defaults","to","2.","name","(","str",",","optional",")",":","the","classifier","name",".","Defaults","to","``","c","''",".","hidden","(","tuple",",","optional",")",":","the","hidden","layer","sizes",".","Defaults","to","(",")",".","activation_fn","(","[","type","]",",","optional",")",":","the","activation","function",".","Defaults","to","nn.ReLU","."]
code: def n_classes(self):
      return self._n_classes
  
imports: import torch
  import torch.nn
  from kale.embed.video_i3d import Unit3D
label_desc: Use image node_id 4 for calling the SoftmaxNet obj's underlying member method code with example usage: obj.n_classes() and returns: self