code: def get_sample_based_on_metric_value(
      self, metric, value_start, value_end
  ):
      new_samples = None
      for row in range(len(self.curriculum_index_to_sample[metric])):
          if (
              self.curriculum_index_to_metric[metric][row] <= value_end
              and self.curriculum_index_to_metric[metric][row]
              > value_start
          ):
              row_samples = np.copy(
                  self.curriculum_index_to_sample[metric][row]
              )
              new_samples = (
                  row_samples
                  if new_samples is None
                  else np.concatenate(
                      (new_samples, row_samples), axis=None
                  )
              )
      return new_samples
  
imports: import torch
  import os
  import numpy
  import deepspeed.comm
  from deepspeed.utils import logger
  from deepspeed.accelerator import get_accelerator
  from ..constants import *
  from ..curriculum_scheduler import CurriculumScheduler
  from .indexed_dataset import MMapIndexedDataset
  from .utils import create_mmap_dataset_builder, close_mmap_dataset_builder, find_fit_int_dtype
label_desc: Use image node_id 5 for calling the DeepSpeedDataSampler obj's underlying member method code with example usage: obj.get_sample_based_on_metric_value(metric, value_start, value_end) and returns: new_samples