code: def load_state_dict(self, state_dict):
      self.batch = state_dict[CURRICULUM_LEARNING_BATCH]
      self.consumed_samples = state_dict[
          CURRICULUM_LEARNING_CONSUMED_SAMPLES
      ]
      self.curriculum_step = state_dict[CURRICULUM_LEARNING_STEP]
      self.current_difficulties = state_dict[
          CURRICULUM_LEARNING_CURRENT_DIFFICULTIES
      ]
      self.data_cluster_paths = state_dict[
          CURRICULUM_LEARNING_DATA_CLUSTER_PATHS
      ]
      self.data_cluster_current_position = state_dict[
          CURRICULUM_LEARNING_DATA_CLUSTER_CURRENT_POSITION
      ]
      np.random.set_state(state_dict[CURRICULUM_LEARNING_NP_RNG_STATE])
      cluster_root_path = self.data_efficiency_config[DATA_SAMPLING][
          CURRICULUM_LEARNING
      ][CURRICULUM_LEARNING_CLUSTER_PATH]
      # Backward compatibility: previously data_cluster_paths were stored as
      # absolute paths. Now we changed it to just the file name so that even
      # if user moved the cluster files, the checkpoint loading still works
      # as long as user set the correct new CURRICULUM_LEARNING_CLUSTER_PATH
      # in deepspeed json config.
      for idx in range(len(self.data_cluster_paths)):
          if "/" in self.data_cluster_paths[idx]:
              self.data_cluster_paths[idx] = self.data_cluster_paths[
                  idx
              ].split("/")[-1]
      if self.global_rank == 0:
          for cluster_fname in self.data_cluster_paths:
              cluster_path = f"{cluster_root_path}/{cluster_fname}"
              self.data_clusters.append(
                  MMapIndexedDataset(cluster_path, skip_warmup=True)
              )
              self.data_cluster_sizes.append(
                  len(self.data_clusters[-1][0])
              )
  
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 14 for calling the DeepSpeedDataSampler obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types