class_docstr: MNASNet, as described in https://arxiv.org/abs/1807.11626. This
  implements the B1 variant of the model.
  >>> model = MNASNet(1.0, num_classes=1000)
  >>> x = torch.rand(1, 3, 224, 224)
  >>> y = model(x)
  >>> y.dim()
  2
  >>> y.nelement()
  1000
code: def forward(self, x: Tensor) -> Tensor:
      x = self.layers(x)
      # Equivalent to global avgpool and removing H and W dimensions.
      x = x.mean([2, 3])
      return self.classifier(x)
  
imports: import warnings
  from functools import partial
  from typing import Any, Dict, List, Optional
  import torch
  import torch.nn
  from torch import Tensor
  from ..transforms._presets import ImageClassification
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
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