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: Returns a batch for each image in the validation set.
  Flattens and splits them to feed it to Keras's model.evaluate.
docstr_tok: ["Returns","a","batch","for","each","image","in","the","validation","set",".","Flattens","and","splits","them","to","feed","it","to","Keras","'s","model.evaluate","."]
code: def get_validation_set(self, batch_size):
      """
      Returns a batch for each image in the validation set.
      Flattens and splits them to feed it to Keras's model.evaluate.
      """
  
      if self.n_validation_samples:
          batches = self.get_validation_batches(batch_size)
          valid_set = {"lr": [], "hr": []}
          for batch in batches:
              for res in ("lr", "hr"):
                  valid_set[res].extend(batch[res])
          for res in ("lr", "hr"):
              valid_set[res] = np.array(valid_set[res])
          return valid_set
      else:
          self.logger.error(
              "No validation set size specified. (not operating in a validation set?)"
          )
          raise ValueError(
              "No validation set size specified. (not operating in a validation set?)"
          )
  
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 11 for calling the DataHandler obj's underlying member method code with example usage: obj.get_validation_set(batch_size) and returns: valid_set