Added IFFT to stateful class for stateful layers

ivy/stateful/layers.py

@@ -2224,3 +2224,58 @@ def _forward(self, x):
             The input array as it is.
         """
         return x
+
+
+class IFFT(Module):
+    def __init__(
+        self,
+        dim,
+        /,
+        *,
+        norm="backward",
+        n=None,
+        out=None,
+        device=None,
+        dtype=None,
+    ):
+        """
+        Class for applying IFFT to input.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension along which to take the IFFT.
+        norm : str
+            Normalization mode. Default: 'backward'
+        n : int
+            Size of the IFFT. Default: None
+        out : int
+            Size of the output. Default: None
+        """
+        self._dim = dim
+        self._norm = norm
+        self._n = n
+        self._out = out
+        Module.__init__(self, device=device, dtype=dtype)
+
+    def _forward(self, inputs):
+        """
+        Forward pass of the layer.
+
+        Parameters
+        ----------
+        inputs : array
+            Input array to take the IFFT of.
+
+        Returns
+        -------
+        array
+            The output array of the layer.
+        """
+        return ivy.ifft(
+            inputs,
+            self._dim,
+            norm=self._norm,
+            n=self._n,
+            out=self._out,
+        )

ivy_tests/test_ivy/test_stateful/test_layers.py

@@ -1198,6 +1198,45 @@ def test_identity_layer(
     )
 
 
+# IFFT
+@handle_method(
+    method_tree="IFFT.__call__",
+    x_and_ifft=exp_layers_tests._x_and_ifft(),
+)
+def test_ifft_layer(
+    *,
+    x_and_ifft,
+    test_gradients,
+    on_device,
+    class_name,
+    method_name,
+    ground_truth_backend,
+    init_flags,
+    method_flags,
+    backend_fw,
+):
+    dtype, x, dim, norm, n = x_and_ifft
+    helpers.test_method(
+        ground_truth_backend=ground_truth_backend,
+        backend_to_test=backend_fw,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        init_all_as_kwargs_np={
+            "dim": dim,
+            "norm": norm,
+            "n": n,
+            "device": on_device,
+            "dtype": dtype[0],
+        },
+        method_input_dtypes=dtype,
+        method_all_as_kwargs_np={"inputs": x[0]},
+        class_name=class_name,
+        method_name=method_name,
+        test_gradients=test_gradients,
+        on_device=on_device,
+    )
+
+
 # linear
 @handle_method(
     method_tree="Linear.__call__",