feat: Add frontend jax.numpy.fft.ifftn

ivy/functional/frontends/jax/numpy/fft.py

@@ -70,6 +70,14 @@ def ifft2(a, s=None, axes=(-2, -1), norm=None):
     return ivy.array(ivy.ifft2(a, s=s, dim=axes, norm=norm), dtype=ivy.dtype(a))
 
 
+@with_unsupported_dtypes({"1.24.3 and below": ("complex64", "bfloat16")}, "numpy")
+@to_ivy_arrays_and_back
+def ifftn(a, s=None, axes=None, norm=None):
+    a = ivy.asarray(a, dtype=ivy.complex128)
+    a = ivy.ifftn(a, s=s, axes=axes, norm=norm)
+    return a
+
+
 @to_ivy_arrays_and_back
 @with_unsupported_dtypes({"1.25.2 and below": ("float16", "bfloat16")}, "numpy")
 def rfft(a, n=None, axis=-1, norm=None):

ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_fft.py

@@ -4,6 +4,9 @@
 # local
 import ivy_tests.test_ivy.helpers as helpers
 from ivy_tests.test_ivy.helpers import handle_frontend_test
+from ivy_tests.test_ivy.test_functional.test_experimental.test_nn.test_layers import (
+    _x_and_ifftn_jax,
+)
 
 
 # fft
@@ -242,6 +245,32 @@ def test_jax_numpy_ifft2(
     )
 
 
+@handle_frontend_test(
+    fn_tree="jax.numpy.fft.ifftn",
+    dtype_and_x=_x_and_ifftn_jax(),
+)
+def test_jax_numpy_ifftn(
+    dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_device
+):
+    input_dtype, x, s, axes, norm = dtype_and_x
+
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        test_values=True,
+        atol=1e-09,
+        rtol=1e-08,
+        a=x,
+        s=s,
+        axes=axes,
+        norm=norm,
+    )
+
+
 # rfft
 @handle_frontend_test(
     fn_tree="jax.numpy.fft.rfft",

ivy_tests/test_ivy/test_functional/test_experimental/test_nn/test_layers.py

@@ -457,6 +457,45 @@ def _x_and_ifftn(draw):
     return dtype, x, s, axes, norm
 
 
+@st.composite
+def _x_and_ifftn_jax(draw):
+    min_fft_points = 2
+    dtype = draw(helpers.get_dtypes("complex"))
+    x_dim = draw(
+        helpers.get_shape(
+            min_dim_size=2, max_dim_size=100, min_num_dims=1, max_num_dims=4
+        )
+    )
+    x = draw(
+        helpers.array_values(
+            dtype=dtype[0],
+            shape=tuple(x_dim),
+            min_value=-1e-10,
+            max_value=1e10,
+        )
+    )
+    axes = draw(
+        st.lists(
+            st.integers(0, len(x_dim) - 1),
+            min_size=1,
+            max_size=min(len(x_dim), 3),
+            unique=True,
+        )
+    )
+    norm = draw(st.sampled_from(["forward", "ortho", "backward"]))
+
+    # Shape for s can be larger, smaller or equal to the size of the input
+    # along the axes specified by axes.
+    # Here, we're generating a list of integers corresponding to each axis in axes.
+    s = draw(
+        st.lists(
+            st.integers(min_fft_points, 256), min_size=len(axes), max_size=len(axes)
+        )
+    )
+
+    return dtype, x, s, axes, norm
+
+
 @st.composite
 def _x_and_rfft(draw):
     min_fft_points = 2