Feat: Added scatter_nd tensorflow frontend function

ivy/functional/frontends/tensorflow/general_functions.py

@@ -400,6 +400,11 @@ def scan(
     return ivy.associative_scan(elems, fn, reverse=reverse)
 
 
+@to_ivy_arrays_and_back
+def scatter_nd(indices, updates, shape, name=None):
+    ivy.scatter_nd(indices, updates, shape)
+
+
 @to_ivy_arrays_and_back
 def searchsorted(sorted_sequence, values, side="left", out_type="int32"):
     out_type = to_ivy_dtype(out_type)

ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py

@@ -367,6 +367,83 @@ def _strided_slice_helper(draw):
     return dtype, x, np.array(begin), np.array(end), np.array(strides), masks
 
 
+@st.composite
+def _values_and_ndindices(
+    draw,
+    *,
+    array_dtypes,
+    indices_dtypes=helpers.get_dtypes("integer"),
+    allow_inf=False,
+    x_min_value=None,
+    x_max_value=None,
+    min_num_dims=2,
+    max_num_dims=5,
+    min_dim_size=1,
+    max_dim_size=10,
+):
+    x_dtype, x, x_shape = draw(
+        helpers.dtype_and_values(
+            available_dtypes=array_dtypes,
+            allow_inf=allow_inf,
+            ret_shape=True,
+            min_value=x_min_value,
+            max_value=x_max_value,
+            min_num_dims=min_num_dims,
+            max_num_dims=max_num_dims,
+            min_dim_size=min_dim_size,
+            max_dim_size=max_dim_size,
+        )
+    )
+    x_dtype = x_dtype[0] if isinstance(x_dtype, (list)) else x_dtype
+    x = x[0] if isinstance(x, (list)) else x
+    # indices_dims defines how far into the array to index.
+    indices_dims = draw(
+        helpers.ints(
+            min_value=1,
+            max_value=len(x_shape) - 1,
+        )
+    )
+
+    # num_ndindices defines the number of elements to generate.
+    num_ndindices = draw(
+        helpers.ints(
+            min_value=1,
+            max_value=x_shape[indices_dims],
+        )
+    )
+
+    # updates_dims defines how far into the array to index.
+    updates_dtype, updates = draw(
+        helpers.dtype_and_values(
+            available_dtypes=array_dtypes,
+            allow_inf=allow_inf,
+            shape=x_shape[indices_dims:],
+            num_arrays=num_ndindices,
+            shared_dtype=True,
+        )
+    )
+    updates_dtype = (
+        updates_dtype[0] if isinstance(updates_dtype, list) else updates_dtype
+    )
+    updates = updates[0] if isinstance(updates, list) else updates
+
+    indices = []
+    indices_dtype = draw(st.sampled_from(indices_dtypes))
+    for _ in range(num_ndindices):
+        nd_index = []
+        for j in range(indices_dims):
+            axis_index = draw(
+                helpers.ints(
+                    min_value=0,
+                    max_value=max(0, x_shape[j] - 1),
+                )
+            )
+            nd_index.append(axis_index)
+        indices.append(nd_index)
+    indices = np.array(indices)
+    return [x_dtype, indices_dtype, updates_dtype], x, indices, updates
+
+
 @st.composite
 def _x_cast_dtype_shape(draw):
     x_dtype = draw(helpers.get_dtypes("valid", full=False))
@@ -1699,6 +1776,33 @@ def _test_fn(a, x):
     )
 
 
+@handle_frontend_test(
+    fn_tree="tensorflow.scatter_nd",
+    x=_values_and_ndindices(
+        array_dtypes=helpers.get_dtypes("numeric"),
+        indices_dtypes=["int32", "int64"],
+        x_min_value=0,
+        x_max_value=0,
+        min_num_dims=2,
+        allow_inf=False,
+    ),
+)
+def test_tensorflow_scatter_nd(x, frontend, backend_fw, test_flags, fn_tree, on_device):
+    (val_dtype, ind_dtype, update_dtype), vals, ind, updates = x
+    shape = vals.shape
+    helpers.test_frontend_function(
+        input_dtypes=[val_dtype, ind_dtype, update_dtype],
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        indices=np.asarray(ind, dtype=ind_dtype),
+        updates=updates,
+        shape=shape,
+    )
+
+
 # searchsorted
 @handle_frontend_test(
     fn_tree="tensorflow.searchsorted",