class_docstr: ImageNet dataset.
class_docstr_tok: ["ImageNet","dataset","."]
code: def _prepare_im_masked(self, im_path):
      with pathmgr.open(im_path, "rb") as f:
          with Image.open(f) as im:
              im = im.convert("RGB")
  
      if self.mode in ["train", "val"]:
          depth = self.cfg.MASK.PRETRAIN_DEPTH[-1]
          assert depth == max(self.cfg.MASK.PRETRAIN_DEPTH)
          max_mask = self.cfg.AUG.MAX_MASK_PATCHES_PER_BLOCK
          # use feat geometry for determining num masks
          mask_window_size = self.feat_size[depth][-1]
          num_mask = round(
              self.feat_size[depth][-1]
              * self.feat_size[depth][-2]
              * self.cfg.AUG.MASK_RATIO
          )
          min_mask = num_mask // 5
  
          train_size = self.cfg.DATA.TRAIN_CROP_SIZE
          mask_generator = MaskingGenerator(
              mask_window_size,
              num_masking_patches=num_mask,
              max_num_patches=max_mask,
              min_num_patches=min_mask,
          )
          aug_transform = transforms_imagenet_train(
              img_size=(train_size, train_size),
              scale=self.cfg.DATA.TRAIN_JITTER_SCALES_RELATIVE,
              ratio=self.cfg.DATA.TRAIN_JITTER_ASPECT_RELATIVE,
              interpolation=self.cfg.AUG.INTERPOLATION,
              color_jitter=self.cfg.AUG.COLOR_JITTER,
              auto_augment=self.cfg.AUG.AA_TYPE,
              re_prob=0.0,
              mean=self.cfg.DATA.MEAN,
              std=self.cfg.DATA.STD,
          )
          im = aug_transform(im)
          mask = mask_generator()
          return [im, torch.Tensor(), mask]
      else:
          raise NotImplementedError
      return aug_transform(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 7 for calling the Imagenet obj's underlying member method code with example usage: obj._prepare_im_masked(im_path) and returns: aug_transform, list