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 _initialize_weights(self):
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              torch_nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
              if m.bias is not None:
                  torch_nn.init.zeros_(m.bias)
          elif isinstance(m, nn.BatchNorm2d):
              torch_nn.init.ones_(m.weight)
              torch_nn.init.zeros_(m.bias)
          elif isinstance(m, nn.Linear):
              torch_nn.init.kaiming_uniform_(
                  m.weight, mode="fan_out", nonlinearity="sigmoid"
              )
              torch_nn.init.zeros_(m.bias)
  
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 3 for calling the MNASNet obj's underlying member method code with example usage: obj._initialize_weights() without return types