feat: implemented ifftshift and corresponding test

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

@@ -78,6 +78,33 @@ def ifftn(a, s=None, axes=None, norm=None):
     return a
 
 
+@to_ivy_arrays_and_back
+def ifftshift(x, axes=None):
+    # Check if an array
+    if not ivy.is_array(x):
+        raise ValueError("Input 'x' must be an  array")
+
+    # Get the shape of x
+    shape = ivy.shape(x)
+
+    # If axes is None, shift all axes
+    if axes is None:
+        axes = tuple(range(x.ndim))
+
+    # Initialize a list to store the shift values
+    shift_values = []
+
+    # Calculate shift values for each axis
+    for axis in axes:
+        axis_size = shape[axis]
+        shift = -ivy.floor(axis_size / 2).astype(ivy.int32)
+        shift_values.append(shift)
+
+    # Perform the shift using Ivy's roll function
+    result = ivy.roll(x, shift_values, axes)
+    return result
+
+
 @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

@@ -271,6 +271,30 @@ def test_jax_numpy_ifftn(
     )
 
 
+# ifftshift
+@handle_frontend_test(
+    fn_tree="jax.numpy.fft.ifftshift",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"), shape=(4,), array_api_dtypes=True
+    ),
+)
+def test_jax_numpy_ifftshift(
+    dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_device
+):
+    input_dtype, arr = 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,
+        x=arr[0],
+        axes=None,
+    )
+
+
 # rfft
 @handle_frontend_test(
     fn_tree="jax.numpy.fft.rfft",