import torch
import torchvision
from torch import nn


def create_effnetb2_model(num_classes:int= 3,
 seed:int= 40):

 weights= torchvision.models.EfficientNet_B2_Weights.DEFAULT
 transforms= weights.transforms()
 model= torchvision.models.efficientnet_b2(weights= weights)

 for param in model.parameters():
    param.requires_grad= False

 torch.manual_seed(seed)

 model.classifier(nn.Sequential(

   nn.Dropout(0.3, inplace=True),
   nn.Linear(in_features= 1408, out_features= num_classes),

 )

 return model, transforms