import torch
import torchvision

from torch import nn

def create_effnetb2_model(num_classes:int=3,
                          seed:int=42):
  # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
  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
    model.classifier=nn.Sequential(
        nn.Dropout(p=0.3,inplace=True),
        nn.Linear(in_features=1408,out_features=num_classes)
    )
  return model,transforms