code: def hybrid_forward(self, F, x, **kwargs):
      size = kwargs["weight"].shape
      fan_in = prod(size[1:])
      multiplier = sqrt(2.0 / fan_in)
  
      out = F.Convolution(
          x,
          kwargs["weight"] * multiplier,
          kwargs["bias"],
          kernel=self.kernel,
          pad=self.padding,
          num_filter=self.channel,
      )
      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 EqualConv2d obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: out