class_docstr: Use PyTorch torchvision transforms as EvaDB transforms.
code: def transform(self, frames: ArrayLike) -> ArrayLike:
      return Compose.__call__(self, frames)
  
imports: import numpy
  import pandas
  from numpy.typing import ArrayLike
  from evadb.functions.abstract.abstract_function import AbstractClassifierFunction, AbstractTransformationFunction
  from evadb.functions.gpu_compatible import GPUCompatible
  from evadb.utils.generic_utils import try_to_import_pillow, try_to_import_torch, try_to_import_torchvision
  from PIL import Image
  from torch import nn
  from torchvision.transforms import Compose, transforms
label_desc: Use image node_id 2 for calling the PytorchAbstractTransformationFunction obj's underlying member method code with example usage: obj.transform(frames) and returns: Compose