diff --git a/tests/numpy/test_compile.py b/tests/numpy/test_compile.py
index 4ef224683..c62c004f7 100644
--- a/tests/numpy/test_compile.py
+++ b/tests/numpy/test_compile.py
@@ -18,6 +18,12 @@ from concrete.numpy.compile import compile_numpy_function, compile_numpy_functio
 # pylint: disable=too-many-lines
 
 
+def data_gen(args):
+    """Helper to create an inputset"""
+    for prod in itertools.product(*args):
+        yield prod
+
+
 def no_fuse_unhandled(x, y):
     """No fuse unhandled"""
     x_intermediate = x + 2.8
@@ -259,10 +265,6 @@ def subtest_compile_and_run_unary_ufunc_correctness(
 
     function = get_function(ufunc, upper_function)
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     function_parameters = {arg_name: EncryptedScalar(Integer(64, False)) for arg_name in ["x", "y"]}
 
     compiler_engine = compile_numpy_function(
@@ -291,10 +293,6 @@ def subtest_compile_and_run_binary_ufunc_correctness(
 
     function = get_function(ufunc, upper_function)
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     function_parameters = {arg_name: EncryptedScalar(Integer(64, True)) for arg_name in ["x", "y"]}
 
     compiler_engine = compile_numpy_function(
@@ -476,7 +474,7 @@ def test_compile_function_multiple_outputs(
 ):
     """Test function compile_numpy_function_into_op_graph for a program with multiple outputs"""
 
-    def data_gen(args):
+    def data_gen_local(args):
         for prod in itertools.product(*args):
             yield tuple(numpy.array(val) for val in prod)
 
@@ -487,7 +485,7 @@ def test_compile_function_multiple_outputs(
     op_graph = compile_numpy_function_into_op_graph(
         function,
         function_parameters,
-        data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
+        data_gen_local(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
         default_compilation_configuration,
     )
 
@@ -528,10 +526,6 @@ def test_compile_and_run_correctness(
 ):
     """Test correctness of results when running a compiled function"""
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     function_parameters = {
         arg_name: EncryptedScalar(Integer(64, False)) for arg_name in list_of_arg_names
     }
@@ -963,10 +957,6 @@ def test_compile_and_run_lut_correctness(
 
     input_ranges = tuple((0, 2 ** input_bit - 1) for input_bit in input_bits)
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     function_parameters = {
         arg_name: EncryptedScalar(Integer(input_bit, False))
         for input_bit, arg_name in zip(input_bits, list_of_arg_names)
@@ -1349,7 +1339,7 @@ def test_compile_function_with_dot(
 
     # This is the exhaust, but if ever we have too long inputs (ie, large 'repeat'),
     # we'll have to take random values, not all values one by one
-    def data_gen(max_for_ij, repeat):
+    def data_gen_local(max_for_ij, repeat):
         iter_i = itertools.product(range(0, max_for_ij + 1), repeat=repeat)
         iter_j = itertools.product(range(0, max_for_ij + 1), repeat=repeat)
         for prod_i, prod_j in itertools.product(iter_i, iter_j):
@@ -1362,7 +1352,7 @@ def test_compile_function_with_dot(
     op_graph = compile_numpy_function_into_op_graph(
         function,
         params,
-        data_gen(max_for_ij, repeat),
+        data_gen_local(max_for_ij, repeat),
         default_compilation_configuration,
     )
     str_of_the_graph = format_operation_graph(op_graph)
@@ -1389,10 +1379,6 @@ def test_compile_with_show_mlir(
 ):
     """Test show_mlir option"""
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     function_parameters = {
         arg_name: EncryptedScalar(Integer(64, False)) for arg_name in list_of_arg_names
     }
@@ -1412,10 +1398,6 @@ def test_compile_too_high_bitwidth(default_compilation_configuration):
     def function(x, y):
         return x + y
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     function_parameters = {
         "x": EncryptedScalar(Integer(64, False)),
         "y": EncryptedScalar(Integer(64, False)),
@@ -1517,10 +1499,6 @@ def test_fail_compile_with_random_inputset(default_compilation_configuration):
 def test_wrong_inputs(default_compilation_configuration):
     """Test compilation with faulty inputs"""
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     # x should have been something like EncryptedScalar(UnsignedInteger(3))
     x = [1, 2, 3]
     input_ranges = ((0, 10),)
@@ -1576,10 +1554,6 @@ def test_compile_and_run_correctness_with_negative_values(
     """Test correctness of results when running a compiled function, which has some negative
     intermediate values."""
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     function_parameters = {
         arg_name: EncryptedScalar(Integer(64, False)) for arg_name in list_of_arg_names
     }
@@ -1614,10 +1588,6 @@ def test_compile_and_run_correctness_with_negative_results(
     results are currently only correct modulo a power of 2 (given by `modulus` parameter). Eg,
     instead of returning -3, the execution may return -3 mod 128 = 125."""
 
-    def data_gen(args):
-        for prod in itertools.product(*args):
-            yield prod
-
     function_parameters = {
         arg_name: EncryptedScalar(Integer(64, False)) for arg_name in list_of_arg_names
     }