class_docstr: A pytorch based classifier. Used to make sure we make maximum
  utilization of features provided by pytorch without reinventing the wheel.
docstr: Given a tensor in GPU, detach and get the numpy output
  Arguments:
       val (Tensor): tensor to be converted
  Returns:
      np.ndarray: numpy array representation
docstr_tok: ["Given","a","tensor","in","GPU",",","detach","and","get","the","numpy","output","Arguments",":","val","(","Tensor",")",":","tensor","to","be","converted","Returns",":","np.ndarray",":","numpy","array","representation"]
code: def as_numpy(self, val) -> np.ndarray:
      """
      Given a tensor in GPU, detach and get the numpy output
      Arguments:
           val (Tensor): tensor to be converted
      Returns:
          np.ndarray: numpy array representation
      """
      return val.detach().cpu().numpy()
  
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 5 for calling the PytorchAbstractClassifierFunction obj's underlying member method code with example usage: obj.as_numpy(val) and returns: val