Update RNN tests and implementation

Author: praveenhosdrug123

keras/src/layers/rnn/rnn.py

@@ -331,6 +331,12 @@ def inner_loop(self, sequences, initial_state, mask, training=False):
             cell_kwargs["training"] = training
 
         def step(inputs, states):
+            """
+            Create new tensor copies when using PyTorch backend
+            with stateful=True. This prevents in-place modifications
+            that would otherwise break PyTorch's autograd functionality
+            by modifying tensors needed for gradient computation.
+            """
             if backend.backend() == "torch" and self.stateful:
                 states = tree.map_structure(ops.copy, states)
             output, new_states = self.cell(inputs, states, **cell_kwargs)

keras/src/layers/rnn/rnn_test.py

@@ -385,21 +385,17 @@ def test_serialization(self):
 
     @pytest.mark.torch
     def test_stateful_state_copying(self):
-        """Test that states are properly copied (not referenced) in PyTorch backend when stateful=True."""
         sequence = np.ones((1, 2, 3))
         layer = layers.RNN(
             TwoStatesRNNCell(2),
             stateful=True,
             return_state=True,
         )
-        
-        # Single forward pass
         _, state1, state2 = layer(sequence)
-        
-        # Check that layer.states contains clones of the states, not references
-        self.assertIsNot(state1, layer.states[0])  # Should be different objects
+
+        self.assertIsNot(state1, layer.states[0])
         self.assertIsNot(state2, layer.states[1])
-        self.assertAllClose(state1, layer.states[0])  # but with same values
+        self.assertAllClose(state1, layer.states[0])
         self.assertAllClose(state2, layer.states[1])
 
     # TODO: test masking