class_docstr: Sample person identities evenly in each batch.
  Args:
      train_color_label, train_thermal_label: labels of two modalities
      color_pos, thermal_pos: positions of each identity
      batchSize: batch size
class_docstr_tok: ["Sample","person","identities","evenly","in","each","batch",".","Args",":","train_color_label",",","train_thermal_label",":","labels","of","two","modalities","color_pos",",","thermal_pos",":","positions","of","each","identity","batchSize",":","batch","size"]
code: def __iter__(self):
      return iter(np.arange(len(self.visIndex)))
  
imports: from PIL import Image
  import os
  import torch.utils.data
  import torchvision.transforms
  from torch.utils.data.sampler import Sampler
  import numpy
label_desc: Use image node_id 2 for calling the IdentitySampler obj's underlying member method code with example usage: obj.__iter__() and returns: iter