code: def hybrid_forward(self, F, x, **kwargs):
      weight = F.pad(
          kwargs["weight"] * self.multiplier,
          mode="constant",
          constant_value=0,
          pad_width=(0, 0, 0, 0, 1, 1, 1, 1),
      )
      weight = (
          weight[:, :, 1:, 1:]
          + weight[:, :, :-1, 1:]
          + weight[:, :, 1:, :-1]
          + weight[:, :, :-1, :-1]
      ) / 4
  
      out = F.Convolution(
          x,
          weight,
          kwargs["bias"],
          kernel=weight.shape[-2:],
          stride=(2, 2),
          pad=self.pad,
          num_filter=weight.shape[1],
          no_bias=False,
      )
      return out
  
imports: import numpy
  from math import sqrt
  import mxnet
  import mxnet.ndarray
  import mxnet.gluon.nn
  from numpy import prod
label_desc: Use image node_id 2 for calling the FusedDownsample obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: out