class_docstr: X3D transformation: 1x1x1, Tx3x3 (channelwise, num_groups=dim_in), 1x1x1,
      augmented with (optional) SE (squeeze-excitation) on the 3x3x3 output.
      T is the temporal kernel size (defaulting to 3)
class_docstr_tok: ["X3D","transformation",":","1x1x1",",","Tx3x3","(","channelwise",",","num_groups=dim_in",")",",","1x1x1",",","augmented","with","(","optional",")","SE","(","squeeze-excitation",")","on","the","3x3x3","output",".","T","is","the","temporal","kernel","size","(","defaulting","to","3",")"]
code: def forward(self, x):
      for block in self.children():
          x = block(x)
      return x
  
imports: import torch
  import torch.nn
  from slowfast.models.common import drop_path
  from slowfast.models.nonlocal_helper import Nonlocal
  from slowfast.models.operators import SE, Swish
label_desc: Use image node_id 3 for calling the X3DTransform obj's underlying member method code with example usage: obj.forward(x) and returns: x