code: def forward(self, x):
      identity = x
      # pointwise group convolution 1
      x = self.activ(self.compress_bn1(self.compress_conv1(x)))
      x = self.c_shuffle(x)
      # merging
      y = self.s_merge_conv(x)
      y = self.s_merge_bn(y)
      y = self.activ(y)
      # depthwise convolution (bottleneck)
      x = self.dw_bn2(self.dw_conv2(x))
      # evolution
      y = self.s_conv(y)
      y = self.s_conv_bn(y)
      y = self.activ(y)
      y = self.s_evolve_conv(y)
      y = self.s_evolve_bn(y)
      y = F.sigmoid(y)
      x = x * y
      # pointwise group convolution 2
      x = self.expand_bn3(self.expand_conv3(x))
      # identity branch
      if self.downsample:
          identity = self.avgpool(identity)
          x = torch.cat((x, identity), dim=1)
      else:
          x = x + identity
      x = self.activ(x)
      return x
  
imports: import numpy
  from torch.autograd import Variable
  from pytorch2keras.converter import pytorch_to_keras
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
label_desc: Use image node_id 2 for calling the MEModule obj's underlying member method code with example usage: obj.forward(x) and returns: x