diff --git a/bindings/pyroot/pythonizations/test/tf_pycallables.py b/bindings/pyroot/pythonizations/test/tf_pycallables.py
index 2a7d6b7246238..1a5611aa859a5 100644
--- a/bindings/pyroot/pythonizations/test/tf_pycallables.py
+++ b/bindings/pyroot/pythonizations/test/tf_pycallables.py
@@ -5,8 +5,8 @@
 Cppyy that creates a C++ wrapper to invoke the Python callable.
 """
 
-import unittest
 import math
+import unittest
 
 import ROOT
 
@@ -23,14 +23,17 @@ class pyf_tf1_callable:
     def __call__(self, x, p):
         return p[0] * x[0] + p[1]
 
+
 def pyf_func(x, pars):
     return pars[0] * x[0] * x[2] + x[1] * pars[1]
 
+
 def pyf_tf1_gauss(x, p):
-    return p[0] * 1.0 / math.sqrt(2.0 * math.pi * p[2]**2) * math.exp(-(x[0] - p[1])**2 / 2.0 / p[2]**2)
+    return p[0] * 1.0 / math.sqrt(2.0 * math.pi * p[2] ** 2) * math.exp(-((x[0] - p[1]) ** 2) / 2.0 / p[2] ** 2)
+
 
 def pyf_tf1_coulomb(x, p):
-    return p[1] * x[0] * x[1] / (p[0]**2) * math.exp(-p[2] / p[0])
+    return p[1] * x[0] * x[1] / (p[0] ** 2) * math.exp(-p[2] / p[0])
 
 
 class TF1(unittest.TestCase):
@@ -73,24 +76,23 @@ def test_callable(self):
         for x in [0.0, -1.0, 42.0]:
             self.assertEqual(f.Eval(x), pycallable([x], [par1, par2]))
 
-
     def test_fitgauss(self):
         """
         Test fitting a histogram to a Python function
         """
         # Gaus function
         f = ROOT.TF1("tf1_fitgauss", pyf_tf1_gauss, -4, 4, 3)
-        f.SetParameter(0, 10.0) # scale
-        f.SetParameter(1, -1.0) # mean
-        f.SetParameter(2, 2.0) # standard deviation
+        f.SetParameter(0, 10.0)  # scale
+        f.SetParameter(1, -1.0)  # mean
+        f.SetParameter(2, 2.0)  # standard deviation
 
         # Sample gauss in histogram
         h = ROOT.TH1F("h", "test", 100, -4, 4)
         h.FillRandom("gaus", 100000)
-        h.Scale(1.0 / 100000.0 * 100.0 / 8.0) # Normalize as density
+        h.Scale(1.0 / 100000.0 * 100.0 / 8.0)  # Normalize as density
 
         # Fit to histogram and get parameters
-        h.Fit( f, "0Q" )
+        h.Fit(f, "0Q")
         scale = f.GetParameter(0)
         mean = f.GetParameter(1)
         std = f.GetParameter(2)
@@ -108,19 +110,15 @@ def test_evalpar(self):
         rtf1_coulomb = ROOT.TF1("my_func", pyf_tf1_coulomb, -10, 10)
 
         # x dataset: 5 pairs of particle charges
-        x = np.array([
-            [1.0, 10, 2.0],
-            [1.5, 10, 2.5],
-            [2.0, 10, 3.0],
-            [2.5, 10, 3.5],
-            [3.0, 10, 4.0]
-        ])
-
-        params = np.array([
-            [1.0],       # Distance between charges r
-            [8.99e9],    # Coulomb constant k (in N·m²/C²)
-            [0.1]        # Additional factor for modulation
-        ])
+        x = np.array([[1.0, 10, 2.0], [1.5, 10, 2.5], [2.0, 10, 3.0], [2.5, 10, 3.5], [3.0, 10, 4.0]])
+
+        params = np.array(
+            [
+                1.0,  # Distance between charges r
+                8.99e9,  # Coulomb constant k (in N·m²/C²)
+                0.1,  # Additional factor for modulation
+            ]
+        )
 
         # Slice to avoid the dummy column of 10's
         res = rtf1_coulomb.EvalPar(x[:, ::2], params)
@@ -128,7 +126,7 @@ def test_evalpar(self):
         for i in range(len(x)):
             expected_value = pyf_tf1_coulomb(x[i, ::2], params)
             self.assertEqual(res[i], expected_value)
-    
+
     def test_evalpar_dynamic(self):
         """
         Test the 2D NumPy pythonizations with dynamic TF1 data dimensions
@@ -139,12 +137,9 @@ def test_evalpar_dynamic(self):
         rtf1_func = ROOT.TF1("my_func", pyf_func, -10, 10)
 
         # x dataset with ndims 3
-        x = np.array([[2., 2, 1],
-                [1., 2, 3],
-                [2., 2, 1],
-                [4., 3, 2]])
+        x = np.array([[2.0, 2, 1], [1.0, 2, 3], [2.0, 2, 1], [4.0, 3, 2]])
 
-        pars = np.array([2., 3.])
+        pars = np.array([2.0, 3.0])
         res = rtf1_func.EvalPar(x, pars)
 
         for i in range(len(x)):
@@ -204,6 +199,5 @@ def test_params(self):
             self.assertEqual(f.Eval(*x), pyf_tf2_params(x, [par1, par2, par3, par4]))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
-
diff --git a/tutorials/CMakeLists.txt b/tutorials/CMakeLists.txt
index 532c43d4b94a1..59ea6e78103be 100644
--- a/tutorials/CMakeLists.txt
+++ b/tutorials/CMakeLists.txt
@@ -349,9 +349,9 @@ else()
   # Keras tutorials. This mirrors the logic in tmva/sofie/test/CMakeLists.txt.
   # TODO: make sure we also support the newest Keras
   set(unsupported_keras_version "3.5.0")
-  if (NOT DEFINED ROOT_KERAS_VERSION)
+  if (ROOT_KERAS_FOUND AND NOT DEFINED ROOT_KERAS_VERSION)
     message(WARNING "Keras found, but version unknown — cannot verify compatibility.")
-  elseif (NOT ROOT_KERAS_VERSION VERSION_LESS ${unsupported_keras_version})
+  elseif (ROOT_KERAS_FOUND AND NOT ROOT_KERAS_VERSION VERSION_LESS ${unsupported_keras_version})
       message(WARNING "Keras version ${ROOT_KERAS_VERSION} is too new for the SOFIE Keras parser (only supports < ${unsupported_keras_version}). Corresponding tutorials will not be tested.")
     set(keras_unsupported TRUE)
   endif()