class_docstr: Create mock images for inference on CPU based on the specifics of the original torch test dataset.
  Uses yield so as to avoid storing everything in the memory.
  Args:
      image_size (List[int]): Size of input image
      total_count (int): Total images in the test dataset
class_docstr_tok: ["Create","mock","images","for","inference","on","CPU","based","on","the","specifics","of","the","original","torch","test","dataset",".","Uses","yield","so","as","to","avoid","storing","everything","in","the","memory",".","Args",":","image_size","(","List","[","int","]",")",":","Size","of","input","image","total_count","(","int",")",":","Total","images","in","the","test","dataset"]
code: def __init__(self, image_size: List[int], total_count: int):
      self.total_count = total_count
      self.image_size = image_size
      self.image = np.ones((*self.image_size, 3)).astype(np.uint8)
  
imports: from typing import Iterable, List
  import numpy
label_desc: Use image node_id 1 to create a new MockImageLoader object  with example: obj = MockImageLoader(image_size, total_count)