code: def hardcorenas_d(pretrained=False, **kwargs) -> MobileNetV3:
      """hardcorenas_D"""
      arch_def = [
          ["ds_r1_k3_s1_e1_c16_nre"],
          [
              "ir_r1_k5_s2_e3_c24_nre_se0.25",
              "ir_r1_k5_s1_e3_c24_nre_se0.25",
          ],
          [
              "ir_r1_k5_s2_e3_c40_nre_se0.25",
              "ir_r1_k5_s1_e4_c40_nre_se0.25",
              "ir_r1_k3_s1_e3_c40_nre_se0.25",
          ],
          [
              "ir_r1_k5_s2_e4_c80_se0.25",
              "ir_r1_k3_s1_e3_c80_se0.25",
              "ir_r1_k3_s1_e3_c80_se0.25",
              "ir_r1_k3_s1_e3_c80_se0.25",
          ],
          [
              "ir_r1_k3_s1_e4_c112_se0.25",
              "ir_r1_k5_s1_e4_c112_se0.25",
              "ir_r1_k3_s1_e3_c112_se0.25",
              "ir_r1_k5_s1_e3_c112_se0.25",
          ],
          [
              "ir_r1_k5_s2_e6_c192_se0.25",
              "ir_r1_k5_s1_e6_c192_se0.25",
              "ir_r1_k5_s1_e6_c192_se0.25",
              "ir_r1_k3_s1_e6_c192_se0.25",
          ],
          ["cn_r1_k1_s1_c960"],
      ]
      model = _gen_hardcorenas(
          pretrained=pretrained,
          variant="hardcorenas_d",
          arch_def=arch_def,
          **kwargs,
      )
      return model
  
file_path: pytorch-image-models/timm/models/hardcorenas.py
imports: from functools import partial
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from ._builder import build_model_with_cfg
  from ._builder import pretrained_cfg_for_features
  from ._efficientnet_blocks import SqueezeExcite
  from ._efficientnet_builder import decode_arch_def, resolve_act_layer, resolve_bn_args, round_channels
  from ._registry import register_model, generate_default_cfgs
  from .mobilenetv3 import MobileNetV3, MobileNetV3Features
label_desc: Use image node_id 6 for calling a global function with example usage: hardcorenas_d(pretrained) and returns: model