docstr: Mock the init logic for ImageNet dataset
  
  Specifically, we replace the samples and targets with random data so that actual dataset is not
  required for testing purposes.
code: def __init__(
      self,
      opts: argparse.Namespace,
      is_training: bool = False,
      is_evaluation: bool = False,
      *args,
      **kwargs,
  ) -> None:
      """Mock the init logic for ImageNet dataset
  
      Specifically, we replace the samples and targets with random data so that actual dataset is not
      required for testing purposes.
      """
      # super() is not called here intentionally.
      self.opts = opts
      self.root = None
      self.images = ["dummy_img_path.jpg" for _ in range(TOTAL_SAMPLES)]
      self.masks = ["dummy_mask_path.png" for _ in range(TOTAL_SAMPLES)]
      self.ignore_label = 255
      self.background_idx = 0
      self.is_training = is_training
      self.is_evaluation = is_evaluation
      self.check_dataset()
  
file_path: ml-cvnets/tests/data/datasets/segmentation/mock_ade20k.py
imports: import argparse
  from typing import Optional
  from PIL import Image
  from data.datasets.segmentation.ade20k import ADE20KDataset
label_desc: Use image node_id 1 to create a new MockADE20KDataset object from inherited base classes: ADE20KDataset with example: obj = MockADE20KDataset(opts, is_training, is_evaluation)