import torch
import torchvision
from torch import nn
from torchvision.models._api import WeightsEnum
from torch.hub import load_state_dict_from_url
def get_state_dict(self, *args, **kwargs):
    kwargs.pop("check_hash")
    return load_state_dict_from_url(self.url, *args, **kwargs)
WeightsEnum.get_state_dict = get_state_dict

def create_effnetb2(num_classes: int=3):
    effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    effnetb2_transforms = effnetb2_weights.transforms()
    effnetb2 = torchvision.models.efficientnet_b2(weights="DEFAULT")
    for param in effnetb2.parameters():
        param.requires_grad = False
    torch.manual_seed(42)
    torch.cuda.manual_seed(42)
    effnetb2.classifier = nn.Sequential(
        nn.Dropout(p=0.3, inplace=True),
        nn.Linear(in_features=1408, out_features=num_classes)
    )
    return effnetb2, effnetb2_transforms