class_docstr: Squeeze and excite layer
  
  Parameters
  ----------
  channel:
      size of the input and output
  reduction:
      magnitude of the compression
  D:
      dimension of the kernels
class_docstr_tok: ["Squeeze","and","excite","layer","Parameters","--","--","--","--","--","channel",":","size","of","the","input","and","output","reduction",":","magnitude","of","the","compression","D",":","dimension","of","the","kernels"]
code: def forward(self, x):
      y = self.pooling(x)
      y = self.fc(y)
      return self.broadcast_mul(x, y)
  
imports: import torch
  import MinkowskiEngine
  import sys
  from .common import NormType, get_norm
  from torch_points3d.core.common_modules import Seq, Identity
label_desc: Use image node_id 2 for calling the SELayer obj's underlying member method code with example usage: obj.forward(x) and returns: self