class_docstr: A fake, ImageNet-like dataset which only actually contains `num_actual_datapoints` independent
  datapoints, but pretends to have the number reported in `__len__`. Used for speed and
  simplicity. Replace with your own ImageNet-like dataset as desired.
class_docstr_tok: ["A","fake",",","ImageNet-like","dataset","which","only","actually","contains","`","num_actual_datapoints","`","independent","datapoints",",","but","pretends","to","have","the","number","reported","in","`","__len__","`",".","Used","for","speed","and","simplicity",".","Replace","with","your","own","ImageNet-like","dataset","as","desired","."]
code: def __len__(self) -> int:
      return 10**6
  
file_path: determined/examples/deepspeed_autotune/torchvision/core_api/script.py
imports: import logging
  import uuid
  from typing import Any, Optional, Tuple
  import attrdict
  import deepspeed
  import numpy
  import torch
  import torch.nn
  from torch.utils.data import Dataset
  from torchvision import models
  import determined
  from determined.pytorch import dsat
label_desc: Use image node_id 2 for calling the RandImageNetDataset obj's underlying member method code with example usage: obj.__len__() and returns: unknown