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 __init__(
      self,
      input_size=512,
      n_channel=100,
      dropout_keep_prob=0.5,
      n_class=8,
  ):
      super(ClassNetVideo, self).__init__()
      self._n_classes = n_class
      self.fc1 = nn.Linear(input_size, n_channel)
      self.bn1 = nn.BatchNorm1d(n_channel)
      self.relu1 = nn.ReLU()
      self.dp1 = nn.Dropout(dropout_keep_prob)
      self.fc2 = nn.Linear(n_channel, n_class)
  
imports: import torch
  import torch.nn
  from kale.embed.video_i3d import Unit3D
label_desc: Use image node_id 1 to create a new ClassNetVideo object from inherited base classes: nn with example: obj = ClassNetVideo(input_size, n_channel, dropout_keep_prob, n_class)