class_docstr: DataHandler generate augmented batches used for training or validation.
  
  Args:
      lr_dir: directory containing the Low Res images.
      hr_dir: directory containing the High Res images.
      patch_size: integer, size of the patches extracted from LR images.
      scale: integer, upscaling factor.
      n_validation_samples: integer, size of the validation set. Only provided if the
          DataHandler is used to generate validation sets.
docstr: Creates a dictionary of lists of the acceptable images contained in lr_dir and hr_dir. 
docstr_tok: ["Creates","a","dictionary","of","lists","of","the","acceptable","images","contained","in","lr_dir","and","hr_dir","."]
code: def _make_img_list(self):
      """Creates a dictionary of lists of the acceptable images contained in lr_dir and hr_dir."""
  
      for res in ["hr", "lr"]:
          file_names = os.listdir(self.folders[res])
          file_names = [
              file
              for file in file_names
              if file.endswith(self.extensions)
          ]
          self.img_list[res] = np.sort(file_names)
  
      if self.n_validation_samples:
          samples = np.random.choice(
              range(len(self.img_list["hr"])),
              self.n_validation_samples,
              replace=False,
          )
          for res in ["hr", "lr"]:
              self.img_list[res] = self.img_list[res][samples]
  
file_path: image-super-resolution/ISR/utils/datahandler.py
imports: import os
  import imageio
  import numpy
  from ISR.utils.logger import get_logger
label_desc: Use image node_id 2 for calling the DataHandler obj's underlying member method code with example usage: obj._make_img_list() without return types