code: def get_transform(kind, image_modality):
      """
      Define transforms (for commonly used datasets)
  
      Args:
          kind ([type]): the dataset (transformation) name
          image_modality (string): image type (RGB or Optical Flow)
      """
  
      if kind in ["epic", "gtea", "adl", "kitchen"]:
          transform = dict()
          if image_modality == "rgb":
              transform = {
                  "train": transforms.Compose(
                      [
                          ImglistToTensor(),
                          transforms.Resize(size=256),
                          # transforms.CenterCrop(size=224),
                          transforms.RandomCrop(size=224),
                          transforms.Normalize(
                              mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]
                          ),
                          TensorPermute(),
                      ]
                  ),
                  "valid": transforms.Compose(
                      [
                          ImglistToTensor(),
                          transforms.Resize(size=256),
                          transforms.CenterCrop(size=224),
                          transforms.Normalize(
                              mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]
                          ),
                          TensorPermute(),
                      ]
                  ),
                  "test": transforms.Compose(
                      [
                          ImglistToTensor(),
                          transforms.Resize(size=256),
                          transforms.CenterCrop(size=224),
                          transforms.Normalize(
                              mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]
                          ),
                          TensorPermute(),
                      ]
                  ),
              }
          elif image_modality == "flow":
              transform = {
                  "train": transforms.Compose(
                      [
                          # Stack(),
                          ImglistToTensor(),
                          transforms.Resize(size=256),
                          transforms.RandomCrop(size=224),
                          transforms.Normalize(
                              mean=[0.5, 0.5], std=[0.5, 0.5]
                          ),
                          TensorPermute(),
                      ]
                  ),
                  "valid": transforms.Compose(
                      [
                          # Stack(),
                          ImglistToTensor(),
                          transforms.Resize(size=256),
                          transforms.CenterCrop(size=224),
                          transforms.Normalize(
                              mean=[0.5, 0.5], std=[0.5, 0.5]
                          ),
                          TensorPermute(),
                      ]
                  ),
                  "test": transforms.Compose(
                      [
                          # Stack(),
                          ImglistToTensor(),
                          transforms.Resize(size=256),
                          transforms.CenterCrop(size=224),
                          transforms.Normalize(
                              mean=[0.5, 0.5], std=[0.5, 0.5]
                          ),
                          TensorPermute(),
                      ]
                  ),
              }
          else:
              raise ValueError(
                  "Input modality is not in [rgb, flow, joint]. Current is {}".format(
                      image_modality
                  )
              )
      else:
          raise ValueError(f"Unknown transform kind '{kind}'")
      return transform
  
file_path: pykale/kale/prepdata/video_transform.py
imports: import torch
  from torchvision import transforms
label_desc: Use image node_id 1 for calling a global function with example usage: get_transform(kind, image_modality) and returns: transform