class_docstr: Simple classification prediction-head block to plug ontop of the 4D output of a CNN.
  
  Args:
      n_class (int, optional): the number of different classes that can be predicted. Defaults to 10.
      input_shape (tuples, optional): the shape that input to this head will have. Expected
                    to be (batch_size, channels, height, width). Defaults to (-1, 64, 8, 8).
class_docstr_tok: ["Simple","classification","prediction-head","block","to","plug","ontop","of","the","4D","output","of","a","CNN",".","Args",":","n_class","(","int",",","optional",")",":","the","number","of","different","classes","that","can","be","predicted",".","Defaults","to","10.","input_shape","(","tuples",",","optional",")",":","the","shape","that","input","to","this","head","will","have",".","Expected","to","be","(","batch_size",",","channels",",","height",",","width",")",".","Defaults","to","(","-1",",","64",",","8",",","8",")","."]
code: def __init__(self, n_class=10, input_shape=(-1, 64, 8, 8)):
      super(ClassNet, self).__init__()
      self.avgpool = nn.AvgPool2d(input_shape[2])
      self.linear = nn.Linear(input_shape[1], 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 ClassNet object from inherited base classes: nn with example: obj = ClassNet(n_class, input_shape)