diff --git a/pygmtools/classic_solvers.py b/pygmtools/classic_solvers.py
index 7109d4d..e5e5f8f 100644
--- a/pygmtools/classic_solvers.py
+++ b/pygmtools/classic_solvers.py
@@ -371,10 +371,10 @@ def sm(K, n1=None, n2=None, n1max=None, n2max=None, x0=None,
     if _check_shape(K, 2, backend):
         K = _unsqueeze(K, 0, backend)
         non_batched_input = True
-        if type(n1) is int and n1max is None:
+        if isinstance(n1, (int, np.integer)) and n1max is None:
             n1max = n1
             n1 = None
-        if type(n2) is int and n2max is None:
+        if isinstance(n2, (int, np.integer)) and n2max is None:
             n2max = n2
             n2 = None
     elif _check_shape(K, 3, backend):
@@ -727,10 +727,10 @@ def rrwm(K, n1=None, n2=None, n1max=None, n2max=None, x0=None,
     if _check_shape(K, 2, backend):
         K = _unsqueeze(K, 0, backend)
         non_batched_input = True
-        if type(n1) is int and n1max is None:
+        if isinstance(n1, (int, np.integer)) and n1max is None:
             n1max = n1
             n1 = None
-        if type(n2) is int and n2max is None:
+        if isinstance(n2, (int, np.integer)) and n2max is None:
             n2max = n2
             n2 = None
     elif _check_shape(K, 3, backend):
@@ -1034,10 +1034,10 @@ def ipfp(K, n1=None, n2=None, n1max=None, n2max=None, x0=None,
     if _check_shape(K, 2, backend):
         K = _unsqueeze(K, 0, backend)
         non_batched_input = True
-        if type(n1) is int and n1max is None:
+        if isinstance(n1, (int, np.integer)) and n1max is None:
             n1max = n1
             n1 = None
-        if type(n2) is int and n2max is None:
+        if isinstance(n2, (int, np.integer)) and n2max is None:
             n2max = n2
             n2 = None
     elif _check_shape(K, 3, backend):
@@ -1165,10 +1165,10 @@ def astar(K, n1=None, n2=None, n1max=None, n2max=None, beam_width=0, backend=Non
     if _check_shape(K, 2, backend):
         K = _unsqueeze(K, 0, backend)
         non_batched_input = True
-        if type(n1) is int and n1max is None:
+        if isinstance(n1, (int, np.integer)) and n1max is None:
             n1max = n1
             n1 = None
-        if type(n2) is int and n2max is None:
+        if isinstance(n2, (int, np.integer)) and n2max is None:
             n2max = n2
             n2 = None
     elif _check_shape(K, 3, backend):
diff --git a/pygmtools/linear_solvers.py b/pygmtools/linear_solvers.py
index 1186d8a..c84de68 100644
--- a/pygmtools/linear_solvers.py
+++ b/pygmtools/linear_solvers.py
@@ -729,8 +729,8 @@ def sinkhorn(s, n1=None, n2=None, unmatch1=None, unmatch2=None,
     _check_data_type(s, 's', backend)
     if _check_shape(s, 2, backend):
         s = _unsqueeze(s, 0, backend)
-        if type(n1) is int: n1 = from_numpy(np.array([n1]), backend=backend)
-        if type(n2) is int: n2 = from_numpy(np.array([n2]), backend=backend)
+        if isinstance(n1, (int, np.integer)): n1 = from_numpy(np.array([n1]), backend=backend)
+        if isinstance(n2, (int, np.integer)): n2 = from_numpy(np.array([n2]), backend=backend)
         non_batched_input = True
     elif _check_shape(s, 3, backend):
         non_batched_input = False
@@ -1298,8 +1298,8 @@ def hungarian(s, n1=None, n2=None, unmatch1=None, unmatch2=None,
     _check_data_type(s, backend)
     if _check_shape(s, 2, backend):
         s = _unsqueeze(s, 0, backend)
-        if type(n1) is int: n1 = from_numpy(np.array([n1]), backend=backend)
-        if type(n2) is int: n2 = from_numpy(np.array([n2]), backend=backend)
+        if isinstance(n1, (int, np.integer)): n1 = from_numpy(np.array([n1]), backend=backend)
+        if isinstance(n2, (int, np.integer)): n2 = from_numpy(np.array([n2]), backend=backend)
         non_batched_input = True
     elif _check_shape(s, 3, backend):
         non_batched_input = False
diff --git a/pygmtools/neural_solvers.py b/pygmtools/neural_solvers.py
index f7d7e2f..80629a4 100644
--- a/pygmtools/neural_solvers.py
+++ b/pygmtools/neural_solvers.py
@@ -285,8 +285,8 @@ def pca_gm(feat1, feat2, A1, A2, n1=None, n2=None,
 
         if all([_check_shape(_, 2, backend) for _ in (feat1, feat2, A1, A2)]):
             feat1, feat2, A1, A2 = [_unsqueeze(_, 0, backend) for _ in (feat1, feat2, A1, A2)]
-            if type(n1) is int: n1 = from_numpy(np.array([n1]), backend=backend)
-            if type(n2) is int: n2 = from_numpy(np.array([n2]), backend=backend)
+            if isinstance(n1, (int, np.integer)): n1 = from_numpy(np.array([n1]), backend=backend)
+            if isinstance(n2, (int, np.integer)): n2 = from_numpy(np.array([n2]), backend=backend)
             non_batched_input = True
         elif all([_check_shape(_, 3, backend) for _ in (feat1, feat2, A1, A2)]):
             non_batched_input = False
@@ -592,8 +592,8 @@ def ipca_gm(feat1, feat2, A1, A2, n1=None, n2=None,
 
         if all([_check_shape(_, 2, backend) for _ in (feat1, feat2, A1, A2)]):
             feat1, feat2, A1, A2 = [_unsqueeze(_, 0, backend) for _ in (feat1, feat2, A1, A2)]
-            if type(n1) is int: n1 = from_numpy(np.array([n1]), backend=backend)
-            if type(n2) is int: n2 = from_numpy(np.array([n2]), backend=backend)
+            if isinstance(n1, (int, np.integer)): n1 = from_numpy(np.array([n1]), backend=backend)
+            if isinstance(n2, (int, np.integer)): n2 = from_numpy(np.array([n2]), backend=backend)
             non_batched_input = True
         elif all([_check_shape(_, 3, backend) for _ in (feat1, feat2, A1, A2)]):
             non_batched_input = False
@@ -913,8 +913,8 @@ def cie(feat_node1, feat_node2, A1, A2, feat_edge1, feat_edge2, n1=None, n2=None
                 and all([_check_shape(_, 3, backend) for _ in (feat_edge1, feat_edge2)]):
             feat_node1, feat_node2, A1, A2, feat_edge1, feat_edge2 =\
                 [_unsqueeze(_, 0, backend) for _ in (feat_node1, feat_node2, A1, A2, feat_edge1, feat_edge2)]
-            if type(n1) is int: n1 = from_numpy(np.array([n1]), backend=backend)
-            if type(n2) is int: n2 = from_numpy(np.array([n2]), backend=backend)
+            if isinstance(n1, (int, np.integer)): n1 = from_numpy(np.array([n1]), backend=backend)
+            if isinstance(n2, (int, np.integer)): n2 = from_numpy(np.array([n2]), backend=backend)
             non_batched_input = True
         elif all([_check_shape(_, 3, backend) for _ in (feat_node1, feat_node2, A1, A2)]) \
                 and all([_check_shape(_, 4, backend) for _ in (feat_edge1, feat_edge2)]):
@@ -1239,10 +1239,10 @@ def ngm(K, n1=None, n2=None, n1max=None, n2max=None, x0=None,
         if _check_shape(K, 2, backend):
             K = _unsqueeze(K, 0, backend)
             non_batched_input = True
-            if type(n1) is int and n1max is None:
+            if isinstance(n1, (int, np.integer)) and n1max is None:
                 n1max = n1
                 n1 = None
-            if type(n2) is int and n2max is None:
+            if isinstance(n2, (int, np.integer)) and n2max is None:
                 n2max = n2
                 n2 = None
         elif _check_shape(K, 3, backend):
@@ -1444,8 +1444,8 @@ def genn_astar(feat1, feat2, A1, A2, n1=None, n2=None, channel=None, filters_1=6
 
         if all([_check_shape(_, 2, backend) for _ in (feat1, feat2, A1, A2)]):
             feat1, feat2, A1, A2 = [_unsqueeze(_, 0, backend) for _ in (feat1, feat2, A1, A2)]
-            if type(n1) is int: n1 = from_numpy(np.array([n1]), backend=backend)
-            if type(n2) is int: n2 = from_numpy(np.array([n2]), backend=backend)
+            if isinstance(n1, (int, np.integer)): n1 = from_numpy(np.array([n1]), backend=backend)
+            if isinstance(n2, (int, np.integer)): n2 = from_numpy(np.array([n2]), backend=backend)
             non_batched_input = True
         elif all([_check_shape(_, 3, backend) for _ in (feat1, feat2, A1, A2)]):
             non_batched_input = False