import torch
import torchvision
from torch import nn


def create_effnetb2_model(num_classes:int=3,seed:int=42):

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

  #freeze all layers in the base model

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

  #change classifier head with random seed for reproducibility

  torch.manual_seed(seed)
  model.classifier=nn.Sequential(nn.Dropout(p=0.3,inplace=True),
                                 nn.Linear(in_features=1408,out_features=num_classes))
  
  return model,eff_transforms