Fix DeepLearning params modification issue

Author: tomasfryda

h2o-algos/src/test/java/hex/deeplearning/DeepLearningTest.java

@@ -1318,6 +1318,45 @@ public void testCheckpointBackwards() {
     }
   }
 
+  // GH-16717: Checkpoint resumption fails when distribution=AUTO gets resolved during training.
+  // Training mutates the caller's parms object (AUTO -> multinomial), so cloning after training
+  // picks up the resolved value. Checkpoint validation then sees AUTO (from _input_parms) vs
+  // multinomial (from the cloned parms) and incorrectly throws.
+  @Test
+  public void testCheckpointAutoDistribution() {
+    Frame tfr = null;
+    DeepLearningModel dl = null;
+    DeepLearningModel dl2 = null;
+
+    try {
+      tfr = parseTestFile("./smalldata/iris/iris.csv");
+      DeepLearningParameters parms = new DeepLearningParameters();
+      parms._train = tfr._key;
+      parms._epochs = 1;
+      parms._response_column = "C5";
+      parms._reproducible = true;
+      parms._hidden = new int[]{2, 2};
+      parms._seed = 0xdecaf;
+
+      // distribution defaults to AUTO; training resolves it to multinomial and mutates parms
+      dl = new DeepLearning(parms).trainModel().get();
+
+      // Clone AFTER training — parms._distribution is now multinomial (mutated by training)
+      DeepLearningParameters parms2 = (DeepLearningParameters) parms.clone();
+      parms2._epochs = 2;
+      parms2._checkpoint = dl._key;
+
+      // This should succeed but fails with:
+      // "Cannot change parameter: '_distribution': AUTO -> multinomial"
+      dl2 = new DeepLearning(parms2).trainModel().get();
+      Assert.assertTrue(dl2.epoch_counter > dl.epoch_counter);
+    } finally {
+      if (tfr != null) tfr.delete();
+      if (dl != null) dl.delete();
+      if (dl2 != null) dl2.delete();
+    }
+  }
+
   @Test
   public void testConvergenceLogloss() {
     Frame tfr = null;

h2o-core/src/main/java/hex/ModelBuilder.java

@@ -78,16 +78,16 @@ protected ModelBuilder(P parms) {
   /** Unique new job and named result key */
   protected ModelBuilder(P parms, Key<M> key) {
     _job = new Job<>(_result = key, parms.javaName(), parms.algoName());
-    _parms = parms;
     _input_parms = (P) parms.clone();
+    _parms = (P) parms.clone();
   }
 
   /** Shared pre-existing Job and unique new result key */
   protected ModelBuilder(P parms, Job<M> job) {
     _job = job;
     _result = defaultKey(parms.algoName());
-    _parms = parms;
     _input_parms = (P) parms.clone();
+    _parms = (P) parms.clone();
   }
 
   /** List of known ModelBuilders with all default args; endlessly cloned by