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","."]
code: def get_num_rays_dict_patch(self, num):
      assert self.num_patch == 1
      assert num == PATCH_SIZE_SQUARED
      return {"num_patch_rays": num}
  
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 4 for calling the HumanRayDataset obj's underlying member method code with example usage: obj.get_num_rays_dict_patch(num) and returns: dict