code: def train_per_sample_transform(self):
      return T.Compose(
          [
              ApplyToKeys(
                  DataKeys.INPUT,
                  T.Compose(
                      [
                          T.ToTensor(),
                          T.Resize(self.image_size),
                          T.Normalize(self.mean, self.std),
                          T.RandomHorizontalFlip(),
                      ]
                  ),
              ),
              ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
          ]
      )
  
file_path: lightning-flash/src/flash/image/classification/input_transform.py
imports: from dataclasses import dataclass
  from typing import Tuple, Union
  import torch
  from torch import nn
  from flash.core.data.io.input import DataKeys
  from flash.core.data.io.input_transform import InputTransform
  from flash.core.data.transforms import ApplyToKeys
  from flash.core.utilities.imports import _ALBUMENTATIONS_AVAILABLE, _TORCHVISION_AVAILABLE, requires
label_desc: Use image node_id 2 for calling the ImageClassificationInputTransform obj's underlying member method code with example usage: obj.train_per_sample_transform() and returns: T