I am unable to reproduce the behavior in torch 1.6.0.

  
      gist.github.com
  
  
    https://gist.github.com/InnovArul/b3c0f7d0cc6cbe593d315beaab062fd9 1
dataloader_shuffle.py
from torch.utils.data import DataLoader, Dataset
import torch, torch.nn as nn
import numpy as np

class DS(Dataset):
    # Constructor
    def __init__(self):
        super().__init__()      
        X = list(np.arange(15000))
        self.x = X
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