code: def _mnasnet(
      alpha: float,
      weights: Optional[WeightsEnum],
      progress: bool,
      **kwargs: Any,
  ) -> MNASNet:
      if weights is not None:
          _ovewrite_named_param(
              kwargs, "num_classes", len(weights.meta["categories"])
          )
  
      model = MNASNet(alpha, **kwargs)
  
      if weights:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
  
      return model
  
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 4 for calling a global function with example usage: _mnasnet(alpha, weights, progress) and returns: model