diff --git a/.github/workflows/generate_documentation.yml b/.github/workflows/generate_documentation.yml
index 706c3ad2..546d27b1 100644
--- a/.github/workflows/generate_documentation.yml
+++ b/.github/workflows/generate_documentation.yml
@@ -30,9 +30,8 @@ jobs:
     - name: Install Sphinx and additional dependences
       run: |
         pip install sphinx sphinx-rtd-theme sphinx-autodoc-typehints sphinx-argparse nbsphinx renku-sphinx-theme
-    - name: Install Pandoc
-      uses: pandoc/actions/setup
     - name: Generate Documentation
+      uses: docker://pandoc/core:2.9
       run: |
         cd docs_source
         make clean
diff --git a/src/sparcscore/processing_test.py b/src/sparcscore/processing_test.py
index d746b14f..161ea9dd 100644
--- a/src/sparcscore/processing_test.py
+++ b/src/sparcscore/processing_test.py
@@ -36,7 +36,7 @@ def test_segment_threshold():
     ), "Output labels and input image shapes are not equal"
 
     # Check if output has the correct dtype (int)
-    assert isinstance(labels.dtype, np.int), "Output label dtype is not integer"
+    assert isinstance(labels.dtype, int), "Output label dtype is not integer"
 
     # Check if values are non-negative
     assert np.all(labels >= 0), "Output labels contain negative values"
@@ -55,7 +55,7 @@ def test_segment_global_threshold():
     ), "Output labels and input image shapes are not equal"
 
     # Check if output has the correct dtype (int)
-    assert isinstance(labels.dtype, np.int), "Output label dtype is not integer"
+    assert isinstance(labels.dtype, int), "Output label dtype is not integer"
 
     # Check if values are non-negative
     assert np.all(labels >= 0), "Output labels contain negative values"
@@ -234,7 +234,7 @@ def test_numba_mask_centroid():
     centers, points_class, ids = numba_mask_centroid(mask)
     expected_centers = np.array([[0.33333333, 1.66666667], [1.5, 1.5]])
     expected_points_class = np.array([3, 2], dtype=np.uint32)
-    expected_ids = np.array([1, 2], dtype=np.int32)
+    expected_ids = np.array([1, 2], dtype=int)
 
     assert np.allclose(centers, expected_centers, atol=1e-6)
     assert np.all(points_class == expected_points_class)