import torchvision
from torch import nn


def create_effnetb2_model(num_classes: int = 101):
    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    # Get EffNetB2 transforms
    transforms = weights.transforms()

    # Setup pretrained model instance
    model = torchvision.models.efficientnet_b2(weights=weights)

    # Freeze the base layers in the model ( this will stop all layers from training)
    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, bias=True),
    )
    return model, transforms