import torch
import torchvision
from torch import nn

def create_eff_model():

  weights_eff=torchvision.models.EfficientNet_B2_Weights.DEFAULT

  transforms=weights_eff.transforms()


  #load model

  eff_b2_mod=torchvision.models.efficientnet_b2(weights=weights_eff)

  for param in eff_b2_mod.parameters():
    param.requires_grad=False


  torch.manual_seed(42)
  torch.cuda.manual_seed(42)

  eff_b2_mod.classifier=nn.Sequential(nn.Dropout(p=0.3,inplace=True),
                                      nn.Linear(in_features=1408,out_features=101,bias=True))


  return eff_b2_mod,transforms