code: import os
  import torch, torchvision, torchtext
  from torch import nn, cuda, backends, FloatTensor, LongTensor, optim
  import torch.nn.functional as F
  from torch.autograd import Variable
  from torch.utils.data import Dataset, TensorDataset
  from torch.nn.init import kaiming_uniform, kaiming_normal
  from torchvision.transforms import Compose
  from torchvision.models import resnet18, resnet34, resnet50, resnet101, resnet152
  from torchvision.models import vgg16_bn, vgg19_bn
  from torchvision.models import densenet121, densenet161, densenet169, densenet201
  
  from .models.resnext_50_32x4d import resnext_50_32x4d
  from .models.resnext_101_32x4d import resnext_101_32x4d
  from .models.resnext_101_64x4d import resnext_101_64x4d
  from .models.wrn_50_2f import wrn_50_2f
  from .models.inceptionresnetv2 import InceptionResnetV2
  from .models.inceptionv4 import InceptionV4
  from .models.nasnet import nasnetalarge
  
  from unet_models import unet11
  
  import warnings
  warnings.filterwarnings('ignore', message='Implicit dimension choice', category=UserWarning)
  
  def children(m): return m if isinstance(m, (list, tuple)) else list(m.children())
  def save_model(m, p): torch.save(m.state_dict(), p)
  def load_model(m, p): m.load_state_dict(torch.load(p, map_location=lambda storage, loc: storage))
  
  def load_pre(pre, f, fn):
      m = f()
      path = os.path.dirname(__file__)
      if pre: load_model(m, f'{path}/weights/{fn}.pth')
      return m
  
  def _fastai_model(name, paper_title, paper_href):
      def add_docs_wrapper(f):
          f.__doc__ = f"""{name} model from
          `"{paper_title}" <{paper_href}>`_
  
          Args:
             pre (bool): If True, returns a model pre-trained on ImageNet
          """
          return f
      return add_docs_wrapper
  
  @_fastai_model('Inception 4', 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning',
                 'https://arxiv.org/pdf/1602.07261.pdf')
  def inception_4(pre): return children(inceptionv4(pretrained=pre))[0]
  
  @_fastai_model('Inception 4', 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning',
                 'https://arxiv.org/pdf/1602.07261.pdf')
  def inceptionresnet_2(pre): return load_pre(pre, InceptionResnetV2, 'inceptionresnetv2-d579a627')
  
  @_fastai_model('ResNeXt 50', 'Aggregated Residual Transformations for Deep Neural Networks',
                 'https://arxiv.org/abs/1611.05431')
  def resnext50(pre): return load_pre(pre, resnext_50_32x4d, 'resnext_50_32x4d')
  
  @_fastai_model('ResNeXt 101_32', 'Aggregated Residual Transformations for Deep Neural Networks',
                 'https://arxiv.org/abs/1611.05431')
  def resnext101(pre): return load_pre(pre, resnext_101_32x4d, 'resnext_101_32x4d')
  
  @_fastai_model('ResNeXt 101_64', 'Aggregated Residual Transformations for Deep Neural Networks',
                 'https://arxiv.org/abs/1611.05431')
  def resnext101_64(pre): return load_pre(pre, resnext_101_64x4d, 'resnext_101_64x4d')
  
  @_fastai_model('Wide Residual Networks', 'Wide Residual Networks',
                 'https://arxiv.org/pdf/1605.07146.pdf')
  def wrn(pre): return load_pre(pre, wrn_50_2f, 'wrn_50_2f')
  
  @_fastai_model('Densenet-121', 'Densely Connected Convolutional Networks',
                 'https://arxiv.org/pdf/1608.06993.pdf')
  def dn121(pre): return children(densenet121(pre))[0]
  
  @_fastai_model('Densenet-169', 'Densely Connected Convolutional Networks',
                 'https://arxiv.org/pdf/1608.06993.pdf')
  def dn161(pre): return children(densenet161(pre))[0]
  
  @_fastai_model('Densenet-161', 'Densely Connected Convolutional Networks',
                 'https://arxiv.org/pdf/1608.06993.pdf')
  def dn169(pre): return children(densenet169(pre))[0]
  
  @_fastai_model('Densenet-201', 'Densely Connected Convolutional Networks',
                 'https://arxiv.org/pdf/1608.06993.pdf')
  def dn201(pre): return children(densenet201(pre))[0]
  
  @_fastai_model('Vgg-16 with batch norm added', 'Very Deep Convolutional Networks for Large-Scale Image Recognition',
                 'https://arxiv.org/pdf/1409.1556.pdf')
  def vgg16(pre): return children(vgg16_bn(pre))[0]
  
  @_fastai_model('Vgg-19 with batch norm added', 'Very Deep Convolutional Networks for Large-Scale Image Recognition',
                 'https://arxiv.org/pdf/1409.1556.pdf')
  def vgg19(pre): return children(vgg19_bn(pre))[0]
  
  @_fastai_model('Vgg-11 with U-Net', 'TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation',
                 'https://arxiv.org/pdf/1801.05746.pdf')
  def ternausnet(pre): return children(unet11(pre))
apis: torch.load