class_docstr: ImageNet dataset.
class_docstr_tok: ["ImageNet","dataset","."]
code: def _prepare_im_tf(self, im_path):
      with pathmgr.open(im_path, "rb") as f:
          with Image.open(f) as im:
              im = im.convert("RGB")
      # Convert HWC/BGR/int to HWC/RGB/float format for applying transforms
      train_size, test_size = (
          self.cfg.DATA.TRAIN_CROP_SIZE,
          self.cfg.DATA.TEST_CROP_SIZE,
      )
  
      if self.mode == "train":
          aug_transform = transforms_imagenet_train(
              img_size=(train_size, train_size),
              color_jitter=self.cfg.AUG.COLOR_JITTER,
              auto_augment=self.cfg.AUG.AA_TYPE,
              interpolation=self.cfg.AUG.INTERPOLATION,
              re_prob=self.cfg.AUG.RE_PROB,
              re_mode=self.cfg.AUG.RE_MODE,
              re_count=self.cfg.AUG.RE_COUNT,
              mean=self.cfg.DATA.MEAN,
              std=self.cfg.DATA.STD,
          )
      else:
          t = []
          if self.cfg.DATA.IN_VAL_CROP_RATIO == 0.0:
              t.append(
                  transforms_tv.Resize(
                      (test_size, test_size),
                      interpolation=transforms_tv.InterpolationMode.BICUBIC,
                  ),
              )
          else:
              size = int(
                  (1.0 / self.cfg.DATA.IN_VAL_CROP_RATIO) * test_size
              )  # = 1/0.875 * test_size
              t.append(
                  transforms_tv.Resize(
                      size,
                      interpolation=transforms_tv.InterpolationMode.BICUBIC,
                  ),  # to maintain same ratio w.r.t. 224 images
              )
              t.append(transforms_tv.CenterCrop(test_size))
          t.append(transforms_tv.ToTensor())
          t.append(
              transforms_tv.Normalize(
                  self.cfg.DATA.MEAN, self.cfg.DATA.STD
              )
          )
          aug_transform = transforms_tv.Compose(t)
      im = aug_transform(im)
      return im
  
imports: import json
  import numpy
  import os
  import random
  import re
  import torch
  import torch.utils.data
  from PIL import Image
  from torchvision import transforms
  import slowfast.datasets.transform
  import slowfast.utils.logging
  from slowfast.models.utils import calc_mvit_feature_geometry
  from slowfast.utils.env import pathmgr
  from .build import DATASET_REGISTRY
  from .transform import MaskingGenerator, transforms_imagenet_train
label_desc: Use image node_id 6 for calling the Imagenet obj's underlying member method code with example usage: obj._prepare_im_tf(im_path) and returns: im