matplotlib
diff --git a/‎lib/matplotlib/axes/_axes.py‎
Lines changed: 4 additions & 4 deletions b/‎lib/matplotlib/axes/_axes.py‎
Lines changed: 4 additions & 4 deletions
diff --git a/‎lib/matplotlib/colorizer.py‎
Lines changed: 2 additions & 2 deletions b/‎lib/matplotlib/colorizer.py‎
Lines changed: 2 additions & 2 deletions
diff --git a/‎lib/matplotlib/image.py‎
Lines changed: 1 addition & 1 deletion b/‎lib/matplotlib/image.py‎
Lines changed: 1 addition & 1 deletion
@@ -6208,8 +6208,8 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,
             # If provided, _pcolorargs will check that X, Y and C have the same shape.
             # Before this check, we need to convert C from shape (K, N, M), where K is
             # the number of variates, to (N, M) with a data type with K fields.
-            data = mcolorizer._ensure_multivariate_data(colorizer_obj.norm.n_input,
-                                                        args[-1])
+            data = mcolorizer._ensure_multivariate_data(args[-1],
+                                                        colorizer_obj.norm.n_input)
             args = (*args[:-1], data)
 
         X, Y, C, shading = self._pcolorargs('pcolor', *args, shading=shading,
@@ -6465,8 +6465,8 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
             # If provided, _pcolorargs will check that X, Y and C have the same shape.
             # Before this check, we need to convert C from shape (K, N, M), where K is
             # the number of variates, to (N, M) with a data type with K fields.
-            data = mcolorizer._ensure_multivariate_data(colorizer_obj.norm.n_input,
-                                                        args[-1])
+            data = mcolorizer._ensure_multivariate_data(args[-1],
+                                                        colorizer_obj.norm.n_input)
             args = (*args[:-1], data)
 
         X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
 
@@ -582,7 +582,7 @@ def set_array(self, A):
             self._A = None
             return
 
-        A = _ensure_multivariate_data(self.norm.n_input, A)
+        A = _ensure_multivariate_data(A, self.norm.n_input)
 
         A = cbook.safe_masked_invalid(A, copy=True)
         if not np.can_cast(A.dtype, float, "same_kind"):
@@ -862,7 +862,7 @@ def _ensure_cmap(cmap, accept_multivariate=False):
     return cm.colormaps[cmap_name]
 
 
-def _ensure_multivariate_data(n_input, data):
+def _ensure_multivariate_data(data, n_input):
     """
     Ensure that the data has dtype with n_input.
     Input data of shape (n_input, n, m) is converted to an array of shape
 
@@ -646,7 +646,7 @@ def _normalize_image_array(A, n_input=1):
         Check validity of image-like input *A* and normalize it to a format suitable for
         Image subclasses.
         """
-        A = mcolorizer._ensure_multivariate_data(n_input, A)
+        A = mcolorizer._ensure_multivariate_data(A, n_input)
         A = cbook.safe_masked_invalid(A, copy=True)
         if n_input == 1:
             if A.dtype != np.uint8 and not np.can_cast(A.dtype, float, "same_kind"):