diff --git a/python/src/pywy/tests/test_dl.py b/python/src/pywy/tests/test_dl.py
index a8f542469..c424bad84 100644
--- a/python/src/pywy/tests/test_dl.py
+++ b/python/src/pywy/tests/test_dl.py
@@ -26,53 +26,55 @@
 from pywy.basic.model.optimizer import GradientDescent
 from pywy.basic.model.option import Option
 from pywy.basic.model.models import DLModel
+from importlib import resources
+from pywy.tests import resources as resources_folder
 
 
-# TODO: fix this test by giving it proper test resources & fixing some type issues with lists.
-@pytest.mark.skip(reason="no way of currently testing this, since we are missing implementations for proper test resources & types in types.py")
 def test_dl_tensorflow():
-    l1 = Linear(4, 64, True)
-    s1 = Sigmoid()
-    l2 = Linear(64, 3, True)
+    with resources.path(resources_folder, "sample_data.md") as resource_path, \
+         resources.path(resources_folder, "wordcount_out_python.txt") as output_path:
+        l1 = Linear(4, 64, True)
+        s1 = Sigmoid()
+        l2 = Linear(64, 3, True)
 
-    s1.with_ops(l1.with_ops(Input(Input.Type.FEATURES)))
-    l2.with_ops(s1)
+        s1.with_ops(l1.with_ops(Input(Input.Type.FEATURES)))
+        l2.with_ops(s1)
 
-    model = DLModel(l2)
+        model = DLModel(l2)
 
-    criterion = CrossEntropyLoss(3)
-    criterion.with_ops(
-        Input(Input.Type.PREDICTED),
-        Input(Input.Type.LABEL, Op.DType.INT32)
-    )
-    acc = Mean(0)
-    acc.with_ops(
-        Cast(Op.DType.FLOAT32).with_ops(
-            Eq().with_ops(
-                ArgMax(1).with_ops(
-                    Input(Input.Type.PREDICTED)
-                ),
-                Input(Input.Type.LABEL, Op.DType.INT32)
+        criterion = CrossEntropyLoss(3)
+        criterion.with_ops(
+            Input(Input.Type.PREDICTED),
+            Input(Input.Type.LABEL, Op.DType.INT32)
+        )
+        acc = Mean(0)
+        acc.with_ops(
+            Cast(Op.DType.FLOAT32).with_ops(
+                Eq().with_ops(
+                    ArgMax(1).with_ops(
+                        Input(Input.Type.PREDICTED)
+                    ),
+                    Input(Input.Type.LABEL, Op.DType.INT32)
+                )
             )
         )
-    )
 
-    optimizer = GradientDescent(0.02)
-    option = Option(criterion, optimizer, 6, 100)
+        optimizer = GradientDescent(0.02)
+        option = Option(criterion, optimizer, 6, 100)
 
-    floats: List[List[int]] = [[5.1, 3.5, 1.4, 0.2]]
+        floats: List[List[float]] = [[5.1, 3.5, 1.4, 0.2]]
 
-    ints: List[List[int]] = [[0, 0, 1, 1, 2, 2]]
+        ints: List[List[int]] = [[0, 0, 1, 1, 2, 2]]
 
-    ctx = WayangContext() \
-        .register({JavaPlugin, SparkPlugin, TensorflowPlugin})
-    trainXSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])
-    trainYSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])
-    testXSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])
+        ctx = WayangContext() \
+            .register({JavaPlugin, SparkPlugin, TensorflowPlugin})
+        trainXSource = ctx.textfile(f"file://{resource_path}").map(lambda x: floats, str, List[List[float]])
+        trainYSource = ctx.textfile(f"file://{resource_path}").map(lambda x: floats, str, List[List[float]])
+        testXSource = ctx.textfile(f"file://{resource_path}").map(lambda x: floats, str, List[List[float]])
 
-    data_quanta = trainXSource.dlTraining(model, option, trainYSource, List[List[float]], List[List[float]]) \
-        .predict(testXSource, List[List[float]], List[List[float]]) \
-        .map(lambda x: "Test", List[List[float]], str) \
-        .store_textfile("file:///var/www/html/data/wordcount-out-python.txt", List[float])
-    
-    assert data_quanta is not None
\ No newline at end of file
+        data_quanta = trainXSource.dlTraining(model, option, trainYSource, List[List[float]], List[List[float]]) \
+            .predict(testXSource, List[List[float]], List[List[float]]) \
+            .map(lambda x: "Test", List[List[float]], str) \
+            .store_textfile(f"file://{output_path}", str)
+        
+        assert data_quanta is not None
\ No newline at end of file
diff --git a/python/src/pywy/types.py b/python/src/pywy/types.py
index cbfa0ee5c..41d825ca3 100644
--- a/python/src/pywy/types.py
+++ b/python/src/pywy/types.py
@@ -194,9 +194,9 @@ def typecheck(input_type: Type[ConstrainedOperatorType]):
     origin = get_origin(input_type)
     args = get_args(input_type)
 
-    if isinstance(input_type, List) and args:
+    if origin is list and args:
         typecheck(args[0])
-    elif isinstance(input_type, Tuple):
+    elif origin is tuple:
         if all(arg in allowed_types for arg in args):
             return
         else: