diff --git a/src/python/detectors/pytorch_data_loader_with_multiple_workers/pytorch_data_loader_with_multiple_workers.py b/src/python/detectors/pytorch_data_loader_with_multiple_workers/pytorch_data_loader_with_multiple_workers.py
index ed8c5e3..359842c 100644
--- a/src/python/detectors/pytorch_data_loader_with_multiple_workers/pytorch_data_loader_with_multiple_workers.py
+++ b/src/python/detectors/pytorch_data_loader_with_multiple_workers/pytorch_data_loader_with_multiple_workers.py
@@ -37,25 +37,32 @@ def pytorch_data_loader_with_multiple_workers_noncompliant():
 
 # {fact rule=pytorch-data-loader-with-multiple-workers@v1.0 defects=0}
 def pytorch_data_loader_with_multiple_workers_compliant(args):
-    import torch.optim
-    import torchvision.datasets as datasets
-    # Data loading code
-    traindir = os.path.join(args.data, 'train')
-    valdir = os.path.join(args.data, 'val')
-    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                                     std=[0.229, 0.224, 0.225])
-
-    train_dataset = datasets.ImageFolder(traindir, imagenet_transforms)
-    train_sampler = torch.utils.data.distributed\
-        .DistributedSampler(train_dataset)
-
-    # Compliant: args.workers value is assigned to num_workers,
-    # but native python 'list/dict' is not used here to store the dataset.
-    train_loader = torch.utils.data.DataLoader(train_dataset,
-                                               batch_size=args.batch_size,
-                                               shuffle=(train_sampler is None),
-                                               num_workers=args.workers,
-                                               pin_memory=True,
-                                               sampler=train_sampler)
+    from torch.utils.data import Dataset, DataLoader
+    import numpy as np
+    import torch
+
+    class DataIter(Dataset):
+        def __init__(self):
+            self.data_np = np.array([x for x in range(24000000)])
+
+        def __len__(self):
+            return len(self.data_np)
+
+        def __getitem__(self, idx):
+            data = self.data_np[idx]
+            data = np.array([data], dtype=np.int64)
+            return torch.tensor(data)
+
+    train_data = DataIter()
+    # Compliant: native python `list/dict` is not used to store the dataset
+    # for non zero `num_workers`.
+    train_loader = DataLoader(train_data, batch_size=300,
+                              shuffle=True,
+                              drop_last=True,
+                              pin_memory=False,
+                              num_workers=8)
 
+    for i, item in enumerate(train_loader):
+        if i % 1000 == 0:
+            print(i)
 # {/fact}