code: def __init__(self, config, is_train=True):
      self.frame_interval = config.DATA.FRAME_INTERVAL
      # for mpi dataset, we convert its order to match with h36m
      self.mpi2h36m = [
          10,
          9,
          8,
          11,
          12,
          13,
          4,
          3,
          2,
          5,
          6,
          7,
          1,
          14,
          15,
          16,
          0,
      ]
      # randomization will lead to inferior performance
      self.scale_path = (
          "../data/mpi_train_scales.pkl"
          if config.USE_GT
          else "../data/mpi_train_scales_pre.pkl"
      )
      self.use_same_norm_2d = config.DATA.USE_SAME_NORM_2D
      self.use_same_norm_3d = config.DATA.USE_SAME_NORM_3D
      self.is_train = is_train
      self.data_path = (
          config.DATA.TRAIN_PATH
          if self.is_train
          else config.DATA.VALID_PATH
      )
      self.head_root_distance = (
          1 / config.TRAIN.CAMERA_SKELETON_DISTANCE
      )
      # whether to use dataset adapted from k[MaÌ]inetics
      self.use_gt = config.USE_GT
      self.use_ideal_scale = config.DATA.USE_IDEAL_SCALE
      self.min_diff_dist = config.DATA.MIN_DIFF_DIST
      self.use_scaler = config.TRAIN.USE_SCALER
      self.bound_azim = float(
          config.TRAIN.BOUND_AZIM
      )  # y axis rotation
      self.bound_elev = float(config.TRAIN.BOUND_ELEV)
      self._load_data_set()
  
imports: import sys
  from torch.utils.data import Dataset, DataLoader
  import os
  import os.path
  import glob
  import numpy
  import random
  import cv2
  import pickle
  import json
  import h5py
  import torch
  import matplotlib.pyplot
  from lib.utils.misc import process_dataset_for_video
label_desc: Use image node_id 1 to create a new MPIINFDataset object from inherited base classes: Dataset with example: obj = MPIINFDataset(config, is_train)