class_docstr: ImageNet dataset.
class_docstr_tok: ["ImageNet","dataset","."]
docstr: Prepares the image for network input with format of CHW RGB float
docstr_tok: ["Prepares","the","image","for","network","input","with","format","of","CHW","RGB","float"]
code: def load_image(self, im_path):
      """Prepares the image for network input with format of CHW RGB float"""
      with pathmgr.open(im_path, "rb") as f:
          with Image.open(f) as im:
              im = im.convert("RGB")
      im = torch.from_numpy(np.array(im).astype(np.float32) / 255.0)
      # H W C to C H W
      im = im.permute([2, 0, 1])
      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 4 for calling the Imagenet obj's underlying member method code with example usage: obj.load_image(im_path) and returns: im