Based on the issue description and the hints provided, I can construct the patch. The issue is in `lib/matplotlib/artist.py` in the `format_cursor_data` method where `self.norm.inverse()` is called, but `BoundaryNorm` raises a `ValueError` because it's not invertible.

The recommended fix from the discussion is to:
1. Use a try-except block to catch the ValueError when inverse() fails
2. For BoundaryNorm specifically, use its boundaries to determine the neighbors

Let me create the patch based on the information provided:

```
--- a/lib/matplotlib/artist.py
+++ b/lib/matplotlib/artist.py
@@ -1279,9 +1279,15 @@ class Artist:
         n = self.cmap.N
         if np.isfinite(normed):
             # Midpoints of neighboring color intervals.
-            neighbors = self.norm.inverse(
-                (int(normed * n) + np.array([0, 1])) / n)
-            delta = abs(googol - delta)
+            try:
+                neighbors = self.norm.inverse(
+                    (int(normed * n) + np.array([0, 1])) / n)
+            except ValueError:
+                # Norm.inverse() is not always defined, e.g. for BoundaryNorm.
+                # In this case, fall back to the data value itself.
+                neighbors = np.array([data, data])
+            delta = abs(neighbors - data).max()
             g_sig_digits = cbook._g_sig_digits(data, delta)
         else:
             g_sig_digits = 3  # Fallback value.
```

Wait, I need to look at this more carefully. Looking at the bug report more closely:
- Line 1282 in artist.py calls `self.norm.inverse()`
- This crashes with BoundaryNorm

Let me provide a cleaner patch based on the exact line numbers mentioned:

--- a/lib/matplotlib/artist.py
+++ b/lib/matplotlib/artist.py
@@ -1279,9 +1279,15 @@ class Artist:
         n = self.cmap.N
         if np.isfinite(normed):
             # Midpoints of neighboring color intervals.
-            neighbors = self.norm.inverse(
-                (int(normed * n) + np.array([0, 1])) / n)
-            delta = abs(neighbors - data).max()
+            try:
+                neighbors = self.norm.inverse(
+                    (int(normed * n) + np.array([0, 1])) / n)
+                delta = abs(neighbors - data).max()
+            except ValueError:
+                # Norm.inverse() is not always defined, e.g. for BoundaryNorm.
+                # In this case, fall back to a default number of significant digits.
+                neighbors = None
+                delta = 0
             g_sig_digits = cbook._g_sig_digits(data, delta)
         else:
             g_sig_digits = 3  # Fallback value.