class_docstr: Classifier network for video input refer to MMSADA.
  
  Args:
      input_size (int, optional): the dimension of the final feature vector. Defaults to 1024.
      n_class (int, optional): the number of classes. Defaults to 8.
  
  References:
      Munro Jonathan, and Dima Damen. "Multi-modal domain adaptation for fine-grained action recognition."
      In CVPR, pp. 122-132. 2020.
class_docstr_tok: ["Classifier","network","for","video","input","refer","to","MMSADA",".","Args",":","input_size","(","int",",","optional",")",":","the","dimension","of","the","final","feature","vector",".","Defaults","to","1024.","n_class","(","int",",","optional",")",":","the","number","of","classes",".","Defaults","to","8",".","References",":","Munro","Jonathan",",","and","Dima","Damen",".","``","Multi-modal","domain","adaptation","for","fine-grained","action","recognition",".","''","In","CVPR",",","pp",".","122-132",".","2020","."]
code: def forward(self, input):
      x = self.logits(self.dp(input))
      return x
  
imports: import torch
  import torch.nn
  from kale.embed.video_i3d import Unit3D
label_desc: Use image node_id 2 for calling the ClassNetVideoConv obj's underlying member method code with example usage: obj.forward(input) and returns: x