matplotlib
diff --git a/‎lib/matplotlib/axes/_axes.py‎
Lines changed: 2 additions & 2 deletions b/‎lib/matplotlib/axes/_axes.py‎
Lines changed: 2 additions & 2 deletions
diff --git a/‎lib/matplotlib/colorizer.py‎
Lines changed: 1 addition & 3 deletions b/‎lib/matplotlib/colorizer.py‎
Lines changed: 1 addition & 3 deletions
diff --git a/‎lib/matplotlib/colors.py‎
Lines changed: 1 addition & 1 deletion b/‎lib/matplotlib/colors.py‎
Lines changed: 1 addition & 1 deletion
diff --git a/‎lib/matplotlib/image.py‎
Lines changed: 1 addition & 1 deletion b/‎lib/matplotlib/image.py‎
Lines changed: 1 addition & 1 deletion
@@ -6377,13 +6377,13 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
             - (M, N) or M*N: a mesh with scalar data. The values are mapped to
               colors using normalization and a colormap. See parameters *norm*,
               *cmap*, *vmin*, *vmax*.
-            - (K, M, N): multiple images with scalar data. Must be used with
+            - (K, M, N): multiple layers with scalar data. Must be used with
               a multivariate or bivariate colormap. See *cmap*.
             - (M, N, 3): an image with RGB values (0-1 float or 0-255 int).
             - (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
               i.e. including transparency.
 
-            Here M and N define the rows and columns of the image.
+            Here M and N define the rows and columns of the mesh.
 
         X, Y : array-like, optional
             The coordinates of the corners of quadrilaterals of a pcolormesh::
 
@@ -505,9 +505,7 @@ def _format_cursor_data_override(self, data):
 
         # scalar data
         n = self.cmap.N
-        g_sig_digits = self._sig_digits_from_norm(self.norm,
-                                                  data,
-                                                  n)
+        g_sig_digits = self._sig_digits_from_norm(self.norm, data, n)
         return f"[{data:-#.{g_sig_digits}g}]"
 
 
 
@@ -3266,7 +3266,7 @@ def __init__(self, norms, vmin=None, vmax=None, clip=False):
         # Convert the list of norms to a tuple to make it immutable.
         # If there is a use case for swapping a single norm, we can add support for
         # that later
-        self._norms = tuple(n for n in norms)
+        self._norms = tuple(norms)
 
         self.callbacks = cbook.CallbackRegistry(signals=["changed"])
 
 
@@ -658,7 +658,7 @@ def _normalize_image_array(A, n_input=1):
                                  " must be explicitly declared, for example"
                                  f" cmap='{A.shape[0]}VarAddA'")
             raise TypeError(f"Invalid shape {A.shape} for image data")
-        if A.ndim == 3:
+        if A.ndim == 3 and n_input == 1:
             # If the input data has values outside the valid range (after
             # normalisation), we issue a warning and then clip X to the bounds
             # - otherwise casting wraps extreme values, hiding outliers and