code: def per_sample_transform(self):
      return T.Compose(
          [
              ApplyToKeys(
                  DataKeys.INPUT,
                  T.Compose(
                      [
                          T.ToTensor(),
                          Kg.Resize((196, 196)),
                          # SPATIAL
                          Ka.RandomHorizontalFlip(p=0.25),
                          Ka.RandomRotation(degrees=90.0, p=0.25),
                          Ka.RandomAffine(
                              degrees=1 * 5.0,
                              shear=1 / 5,
                              translate=1 / 20,
                              p=0.25,
                          ),
                          Ka.RandomPerspective(
                              distortion_scale=1 / 25, p=0.25
                          ),
                          # PIXEL-LEVEL
                          Ka.ColorJitter(
                              brightness=1 / 30, p=0.25
                          ),  # brightness
                          Ka.ColorJitter(
                              saturation=1 / 30, p=0.25
                          ),  # saturation
                          Ka.ColorJitter(
                              contrast=1 / 30, p=0.25
                          ),  # contrast
                          Ka.ColorJitter(hue=1 / 30, p=0.25),  # hue
                          Ka.RandomMotionBlur(
                              kernel_size=2 * (4 // 3) + 1,
                              angle=1,
                              direction=1.0,
                              p=0.25,
                          ),
                          Ka.RandomErasing(
                              scale=(1 / 100, 1 / 50),
                              ratio=(1 / 20, 1),
                              p=0.25,
                          ),
                      ]
                  ),
              ),
              ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
          ]
      )
  
file_path: lightning-flash/examples/image/learn2learn_img_classification_imagenette.py
imports: import warnings
  from dataclasses import dataclass
  from typing import Tuple, Union
  import flash
  import kornia.augmentation
  import kornia.geometry
  import learn2learn
  import torch
  import torchvision.transforms
  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.image import ImageClassificationData, ImageClassifier
label_desc: Use image node_id 1 for calling the ImageClassificationInputTransform obj's underlying member method code with example usage: obj.per_sample_transform() and returns: T