class_docstr: We use random rays from a SINGLE time stamp per batch.
class_docstr_tok: ["We","use","random","rays","from","a","SINGLE","time","stamp","per","batch","."]
docstr: We use a large number to represent the length of the training set, and a small number for the validation set.
docstr_tok: ["We","use","a","large","number","to","represent","the","length","of","the","training","set",",","and","a","small","number","for","the","validation","set","."]
code: def __len__(self):
      """
      We use a large number to represent the length of the training set, and a small number for the validation set.
      """
      if self.dset_type == "train":
          return TRAIN_SET_LENGTH
      elif self.dset_type == "val":
          return VALIDATION_SET_LENGTH
      else:
          raise ValueError
  
file_path: ml-neuman/datasets/human_rays.py
imports: import os
  import random
  import numpy
  import torch
  from torch.utils import data
  from data_io import cache_helper
  from utils import ray_utils
  from utils.constant import PATCH_SIZE, PATCH_SIZE_SQUARED, NEAR_INDEX, FAR_INDEX, TRAIN_SET_LENGTH, VALIDATION_SET_LENGTH
  from data_io import neuman_helper
label_desc: Use image node_id 2 for calling the HumanRayDataset obj's underlying member method code with example usage: obj.__len__() and returns: TRAIN_SET_LENGTH, VALIDATION_SET_LENGTH