class_docstr: A pytorch based classifier. Used to make sure we make maximum
  utilization of features provided by pytorch without reinventing the wheel.
docstr: This method transforms the list of frames by
  running pytorch transforms then splits them into batches.
  
  Arguments:
      frames: List of input frames
  
  Returns:
      DataFrame with features key and associated frame
docstr_tok: ["This","method","transforms","the","list","of","frames","by","running","pytorch","transforms","then","splits","them","into","batches",".","Arguments",":","frames",":","List","of","input","frames","Returns",":","DataFrame","with","features","key","and","associated","frame"]
code: def __call__(self, *args, **kwargs) -> pd.DataFrame:
      """
      This method transforms the list of frames by
      running pytorch transforms then splits them into batches.
  
      Arguments:
          frames: List of input frames
  
      Returns:
          DataFrame with features key and associated frame
      """
  
      frames = args[0]
      if isinstance(frames, pd.DataFrame):
          frames = frames.transpose().values.tolist()[0]
  
      # hardcoding it for now, need to be fixed @xzdandy
      gpu_batch_size = 1
      import torch
  
      tens_batch = torch.cat([self.transform(x) for x in frames]).to(
          self.get_device()
      )
  
      if gpu_batch_size:
          chunks = torch.split(tens_batch, gpu_batch_size)
          outcome = pd.DataFrame()
          for tensor in chunks:
              outcome = pd.concat(
                  [outcome, self.forward(tensor)], ignore_index=True
              )
          return outcome
      else:
          return self.forward(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 4 for calling the PytorchAbstractClassifierFunction obj's underlying member method code with example usage: obj.__call__() and returns: outcome, self