class_docstr: Regular domain 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.
class_docstr_tok: ["Regular","domain","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","."]
code: def __init__(self, input_size=128, n_channel=100):
      super(DomainNetVideo, self).__init__()
  
      self.fc1 = nn.Linear(input_size, n_channel)
      self.bn1 = nn.BatchNorm1d(n_channel)
      self.relu1 = nn.ReLU()
      self.fc2 = nn.Linear(n_channel, 2)
  
imports: import torch
  import torch.nn
  from kale.embed.video_i3d import Unit3D
label_desc: Use image node_id 1 to create a new DomainNetVideo object from inherited base classes: nn with example: obj = DomainNetVideo(input_size, n_channel)