import torch 
import torchvision

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