Update to 6.24.02

ROOT now uses llvm/clang version 9 (updated from version 5)
No longer exclude arch s390x (better supported in llvm/clang 9)
Drop patches accepted upstream or previously backported
Backport some fixes that make more tests work
New subpackages: python{2,3}-distrdf, root-roofit-batchcompute
Require js-jsroot >= 6

root-older-python.patch

@@ -0,0 +1,46 @@
+From 61116151cce8fe5b397555a65f7b55001b8e416b Mon Sep 17 00:00:00 2001
+From: Mattias Ellert <mattias.ellert@physics.uu.se>
+Date: Fri, 23 Apr 2021 21:39:17 +0200
+Subject: [PATCH] Compat with no f-strings
+
+---
+ tutorials/tmva/PyTorch_Generate_CNN_Model.py | 12 ++++++------
+ 1 file changed, 6 insertions(+), 6 deletions(-)
+
+diff --git a/tutorials/tmva/PyTorch_Generate_CNN_Model.py b/tutorials/tmva/PyTorch_Generate_CNN_Model.py
+index 7024112f03..5a314f86dd 100644
+--- a/tutorials/tmva/PyTorch_Generate_CNN_Model.py
++++ b/tutorials/tmva/PyTorch_Generate_CNN_Model.py
+@@ -56,7 +56,7 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
+             # print train statistics
+             running_train_loss += train_loss.item()
+             if i % 4 == 3:    # print every 4 mini-batches
+-                print(f"[{epoch+1}, {i+1}] train loss: {running_train_loss / 4 :.3f}")
++                print("[{}, {}] train loss: {:.3f}".format(epoch+1, i+1, running_train_loss / 4))
+                 running_train_loss = 0.0
+ 
+         if schedule:
+@@ -75,15 +75,15 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
+ 
+             curr_val = running_val_loss / len(val_loader)
+             if save_best:
+-               if best_val==None:
+-                   best_val = curr_val
+-               best_val = save_best(model, curr_val, best_val)
++                if best_val is None:
++                    best_val = curr_val
++                best_val = save_best(model, curr_val, best_val)
+ 
+             # print val statistics per epoch
+-            print(f"[{epoch+1}] val loss: {curr_val :.3f}")
++            print("[{}] val loss: {:.3f}".format(epoch+1, curr_val))
+             running_val_loss = 0.0
+ 
+-    print(f"Finished Training on {epoch+1} Epochs!")
++    print("Finished Training on {} Epochs!".format(epoch+1))
+ 
+     return model
+ 
+-- 
+2.30.2
+