code: def _gen_mobilenet_v3_rw(
      variant: str,
      channel_multiplier: float = 1.0,
      pretrained: bool = False,
      **kwargs,
  ) -> MobileNetV3:
      """Creates a MobileNet-V3 model.
  
      Ref impl: ?
      Paper: https://arxiv.org/abs/1905.02244
  
      Args:
        channel_multiplier: multiplier to number of channels per layer.
      """
      arch_def = [
          # stage 0, 112x112 in
          ["ds_r1_k3_s1_e1_c16_nre_noskip"],  # relu
          # stage 1, 112x112 in
          ["ir_r1_k3_s2_e4_c24_nre", "ir_r1_k3_s1_e3_c24_nre"],  # relu
          # stage 2, 56x56 in
          ["ir_r3_k5_s2_e3_c40_se0.25_nre"],  # relu
          # stage 3, 28x28 in
          [
              "ir_r1_k3_s2_e6_c80",
              "ir_r1_k3_s1_e2.5_c80",
              "ir_r2_k3_s1_e2.3_c80",
          ],  # hard-swish
          # stage 4, 14x14in
          ["ir_r2_k3_s1_e6_c112_se0.25"],  # hard-swish
          # stage 5, 14x14in
          ["ir_r3_k5_s2_e6_c160_se0.25"],  # hard-swish
          # stage 6, 7x7 in
          ["cn_r1_k1_s1_c960"],  # hard-swish
      ]
      model_kwargs = dict(
          block_args=decode_arch_def(arch_def),
          head_bias=False,
          round_chs_fn=partial(
              round_channels, multiplier=channel_multiplier
          ),
          norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
          act_layer=resolve_act_layer(kwargs, "hard_swish"),
          se_layer=partial(SqueezeExcite, gate_layer="hard_sigmoid"),
          **kwargs,
      )
      model = _create_mnv3(variant, pretrained, **model_kwargs)
      return model
  
imports: from functools import partial
  from typing import Callable, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.checkpoint import checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import SelectAdaptivePool2d, Linear, LayerType, PadType, create_conv2d, get_norm_act_layer
  from ._builder import build_model_with_cfg, pretrained_cfg_for_features
  from ._efficientnet_blocks import SqueezeExcite
  from ._efficientnet_builder import BlockArgs, EfficientNetBuilder, decode_arch_def, efficientnet_init_weights, round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
  from ._features import FeatureInfo, FeatureHooks
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
label_desc: Use image node_id 2 for calling a global function with example usage: _gen_mobilenet_v3_rw(variant, channel_multiplier, pretrained) and returns: model