class_docstr: Arguments:
      threshold (float): Threshold for classifier confidence score
class_docstr_tok: ["Arguments",":","threshold","(","float",")",":","Threshold","for","classifier","confidence","score"]
docstr: Performs augmentation on input frame
  Arguments:
      frame (Tensor): Frame on which augmentation needs
      to be performed
  Returns:
      frame (Tensor): Augmented frame
docstr_tok: ["Performs","augmentation","on","input","frame","Arguments",":","frame","(","Tensor",")",":","Frame","on","which","augmentation","needs","to","be","performed","Returns",":","frame","(","Tensor",")",":","Augmented","frame"]
code: def transforms_ed(self, frame):
      """
      Performs augmentation on input frame
      Arguments:
          frame (Tensor): Frame on which augmentation needs
          to be performed
      Returns:
          frame (Tensor): Augmented frame
      """
      from torchvision import transforms
  
      # convert to grayscale, resize and make tensor
      frame = frame.convert("L")
      frame = transforms.functional.resize(frame, (48, 48))
      frame = transforms.functional.to_tensor(frame)
  
      return frame
  
file_path: evadb/evadb/functions/emotion_detector.py
imports: import os
  from typing import List
  import numpy
  import pandas
  from evadb.functions.abstract.pytorch_abstract_function import PytorchAbstractClassifierFunction
  from evadb.utils.generic_utils import try_to_import_pillow, try_to_import_torch, try_to_import_torchvision
label_desc: Use image node_id 4 for calling the EmotionDetector obj's underlying member method code with example usage: obj.transforms_ed(frame) and returns: frame