class_docstr: Regular classifier network for video input.
  
  Args:
      input_size (int, optional): the dimension of the final feature vector. Defaults to 512.
      n_channel (int, optional): the number of channel for Linear and BN layers.
      dropout_keep_prob (int, optional): the dropout probability for keeping the parameters.
      n_class (int, optional): the number of classes. Defaults to 8.
class_docstr_tok: ["Regular","classifier","network","for","video","input",".","Args",":","input_size","(","int",",","optional",")",":","the","dimension","of","the","final","feature","vector",".","Defaults","to","512.","n_channel","(","int",",","optional",")",":","the","number","of","channel","for","Linear","and","BN","layers",".","dropout_keep_prob","(","int",",","optional",")",":","the","dropout","probability","for","keeping","the","parameters",".","n_class","(","int",",","optional",")",":","the","number","of","classes",".","Defaults","to","8","."]
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 2 for calling the ClassNetVideo obj's underlying member method code with example usage: obj.n_classes() and returns: self