import torch
import torchvision
from torch import nn

def create_effnet_b2_model(num_classes : int = 3,
                           seed : int = 42):
    
    effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    effnetb2_transforms = effnetb2_weights.transforms()

    effnetb2  = torchvision.models.efficientnet_b2(weights=effnetb2_weights)

    for p in effnetb2.parameters():
        p.requires_grad = False 

    torch.manual_seed(seed)
    #torch.cuda.manual_seed(seed)
    effnetb2.classifier = nn.Sequential(
                 torch.nn.Dropout(p=0.3,
                                  inplace=True),
                torch.nn.Linear(in_features=1408,
                                out_features=num_classes,
                                bias=True)
)

    return effnetb2, effnetb2_transforms