text: from __future__ import absolute_import
  import torch
  
  from .pcb import *
  from .mlfn import *
  from .hacnn import *
  from .osnet import *
  from .senet import *
  from .mudeep import *
  from .nasnet import *
  from .resnet import *
  from .densenet import *
  from .xception import *
  from .osnet_ain import *
  from .resnetmid import *
  from .shufflenet import *
  from .squeezenet import *
  from .inceptionv4 import *
  from .mobilenetv2 import *
  from .resnet_ibn_a import *
  from .resnet_ibn_b import *
  from .shufflenetv2 import *
  from .inceptionresnetv2 import *
  from .resnet_bnneck import *
  from .mgn import *
  
  __model_factory = {
      # image classification models
      'resnet18': resnet18,
      'resnet34': resnet34,
      'resnet50': resnet50,
      'resnet101': resnet101,
      'resnet152': resnet152,
      'resnext50_32x4d': resnext50_32x4d,
      'resnext101_32x8d': resnext101_32x8d,
      'resnet50_fc512': resnet50_fc512,
      'se_resnet50': se_resnet50,
      'se_resnet50_fc512': se_resnet50_fc512,
      'se_resnet101': se_resnet101,
      'se_resnext50_32x4d': se_resnext50_32x4d,
      'se_resnext101_32x4d': se_resnext101_32x4d,
      'densenet121': densenet121,
      'densenet169': densenet169,
      'densenet201': densenet201,
      'densenet161': densenet161,
      'densenet121_fc512': densenet121_fc512,
      'inceptionresnetv2': inceptionresnetv2,
      'inceptionv4': inceptionv4,
      'xception': xception,
      'resnet50_ibn_a': resnet50_ibn_a,
      'resnet50_ibn_b': resnet50_ibn_b,
      # lightweight models
      'nasnsetmobile': nasnetamobile,
      'mobilenetv2_x1_0': mobilenetv2_x1_0,
      'mobilenetv2_x1_4': mobilenetv2_x1_4,
      'shufflenet': shufflenet,
      'squeezenet1_0': squeezenet1_0,
      'squeezenet1_0_fc512': squeezenet1_0_fc512,
      'squeezenet1_1': squeezenet1_1,
      'shufflenet_v2_x0_5': shufflenet_v2_x0_5,
      'shufflenet_v2_x1_0': shufflenet_v2_x1_0,
      'shufflenet_v2_x1_5': shufflenet_v2_x1_5,
      'shufflenet_v2_x2_0': shufflenet_v2_x2_0,
      # reid-specific models
      'mudeep': MuDeep,
      'resnet50mid': resnet50mid,
      'hacnn': HACNN,
      'pcb_p6': pcb_p6,
      'pcb_p4': pcb_p4,
      'mlfn': mlfn,
      'osnet_x1_0': osnet_x1_0,
      'osnet_x0_75': osnet_x0_75,
      'osnet_x0_5': osnet_x0_5,
      'osnet_x0_25': osnet_x0_25,
      'osnet_ibn_x1_0': osnet_ibn_x1_0,
      'osnet_ain_x1_0': osnet_ain_x1_0,
      'resnet_bnneck': resnet_bnneck,
      'mgn': MultiGranularityNet
  }
  
  
  def show_avai_models():
      """Displays available models.
  
      Examples::
          >>> from torchreid import models
          >>> models.show_avai_models()
      """
      print(list(__model_factory.keys()))
  
  
  def build_model(
          name, num_classes, loss='softmax', pretrained=True, use_gpu=True
  ):
      """A function wrapper for building a model.
  
      Args:
          name (str): model name.
          num_classes (int): number of training identities.
          loss (str, optional): loss function to optimize the model. Currently
              supports "softmax" and "triplet". Default is "softmax".
          pretrained (bool, optional): whether to load ImageNet-pretrained weights.
              Default is True.
          use_gpu (bool, optional): whether to use gpu. Default is True.
  
      Returns:
          nn.Module
  
      Examples::
          >>> from torchreid import models
          >>> model = models.build_model('resnet50', 751, loss='softmax')
      """
      avai_models = list(__model_factory.keys())
      if name not in avai_models:
          raise KeyError(
              'Unknown model: {}. Must be one of {}'.format(name, avai_models)
          )
      return __model_factory[name](
          num_classes=num_classes,
          loss=loss,
          pretrained=pretrained,
          use_gpu=use_gpu
      )
  