class_docstr: MNASNet, as described in https://arxiv.org/pdf/1807.11626.pdf. This
  implements the B1 variant of the model.
  >>> model = MNASNet(1000, 1.0)
  >>> x = torch.rand(1, 3, 224, 224)
  >>> y = model(x)
  >>> y.dim()
  1
  >>> y.nelement()
  1000
code: def forward(self, x):
      # if self.for_test == 10:
      x = self.layers(x)
      # Equivalent to global avgpool and removing H and W dimensions.
      x = x.mean([2, 3])
      x = F.relu(x)
      return self.classifier(x)
  
imports: from nni.retiarii import basic_unit
  import nni.retiarii.nn.pytorch
  import warnings
  import torch
  import torch.nn
  import torch.nn.functional
  from nni.retiarii import model_wrapper
  import sys
  from pathlib import Path
label_desc: Use image node_id 2 for calling the MNASNet obj's underlying member method code with example usage: obj.forward(x) and returns: self