ELU added to Ivy Stateful Api (#19820)

Co-authored-by: tejeshbhalla <[email protected]>

ivy/stateful/activations.py

@@ -367,3 +367,24 @@ def _forward(self, x):
             The outputs following the SELU activation *[batch_shape, d]*
         """
         return ivy.selu(x)
+
+
+class ELU(Module):
+    def __init__(self):
+        """Apply the ELU activation function."""
+        Module.__init__(self)
+
+    def _forward(self, x, alpha=1.0):
+        """
+        Parameters
+        ----------
+        x
+            Inputs to process *[batch_shape, d]*.
+        alpha
+            scaler for controlling the slope of the function for x <= 0 Default: 1.0
+        Returns
+        -------
+        ret
+            The outputs following the ELU activation *[batch_shape, d]*
+        """
+        return ivy.elu(x, alpha=alpha)

ivy_tests/test_ivy/test_stateful/test_activations.py

@@ -708,3 +708,49 @@ def test_selu(
         test_gradients=test_gradients,
         on_device=on_device,
     )
+
+
+# ELU
+@handle_method(
+    method_tree="stateful.activations.ELU.__call__",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        num_arrays=2,
+        shared_dtype=True,
+        min_num_dims=2,
+        large_abs_safety_factor=8,
+        small_abs_safety_factor=8,
+        safety_factor_scale="log",
+    ),
+    method_num_positional_args=helpers.num_positional_args(fn_name="ELU._forward"),
+    test_gradients=st.just(True),
+    alpha=helpers.floats(min_value=0.1, max_value=1),
+)
+def test_elu(
+    *,
+    dtype_and_x,
+    alpha,
+    test_gradients,
+    class_name,
+    method_name,
+    ground_truth_backend,
+    init_flags,
+    method_flags,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_method(
+        ground_truth_backend=ground_truth_backend,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        init_input_dtypes=input_dtype,
+        method_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={},
+        method_all_as_kwargs_np={"x": x[0], "alpha": alpha},
+        class_name=class_name,
+        method_name=method_name,
+        rtol_=1e-2,
+        atol_=1e-2,
+        test_gradients=test_gradients,
+        on_device=on_device,
+    )