diff --git a/ivy/functional/backends/tensorflow/general.py b/ivy/functional/backends/tensorflow/general.py
index 0346b00b9641e..275770f162009 100644
--- a/ivy/functional/backends/tensorflow/general.py
+++ b/ivy/functional/backends/tensorflow/general.py
@@ -316,6 +316,8 @@ def scatter_flat(
     if ivy.exists(size) and ivy.exists(target):
         ivy.utils.assertions.check_equal(len(target.shape), 1, as_array=False)
         ivy.utils.assertions.check_equal(target.shape[0], size, as_array=False)
+    if target_given:
+        updates = ivy.astype(updates, target.dtype)
     if not target_given:
         target = tf.zeros([size], dtype=updates.dtype)
         res = tf.tensor_scatter_nd_update(target, tf.expand_dims(indices, -1), updates)
diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
index e1abd35a45c64..2a78520a9a9f4 100644
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -1,6 +1,7 @@
 # global
 from typing import Iterable
 import math
+from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
 
 # local
 import ivy
@@ -2336,6 +2337,10 @@ def minimum(self, other, *, out=None):
     def rad2deg(self, *, out=None):
         return torch_frontend.rad2deg(self, out=out)
 
+    def fill_diagonal_(self, fill_value, wrap=False):
+        self._ivy_array = ivy.fill_diagonal(self._ivy_array, fill_value, wrap=wrap)
+        return self
+
     @with_supported_dtypes(
         {"2.2 and below": "valid"},
         "torch",
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
index a1fe83a3b5d22..960095e9552c8 100644
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
@@ -6907,6 +6907,49 @@ def test_torch_fill_(
     )
 
 
+# fill_diagonal_
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="fill_diagonal_",
+    dtype_x_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_num_dims=2,
+        min_dim_size=2,
+        max_num_dims=2,
+    ),
+    val=helpers.floats(min_value=-10, max_value=10),
+    wrap=st.booleans(),
+)
+def test_torch_fill_diagonal_(
+    dtype_x_axis,
+    wrap,
+    val,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x, axis = dtype_x_axis
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        init_all_as_kwargs_np={"data": x[0]},
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "fill_value": val,
+            "wrap": wrap,
+        },
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
+
+
 # fix
 @handle_frontend_method(
     class_tree=CLASS_TREE,