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 __init__(self, input_size=1024, n_class=8):
      super(ClassNetVideoConv, self).__init__()
      self.dp = nn.Dropout()
      self.logits = Unit3D(
          in_channels=input_size,
          output_channels=n_class,
          kernel_shape=[1, 1, 1],
          padding=0,
          activation_fn=None,
          use_batch_norm=False,
          use_bias=True,
      )
  
imports: import torch
  import torch.nn
  from kale.embed.video_i3d import Unit3D
label_desc: Use image node_id 1 to create a new ClassNetVideoConv object from inherited base classes: nn with example: obj = ClassNetVideoConv(input_size, n_class)