Added true_divide_ to the to PyTorch frontend

ivy/functional/frontends/torch/tensor.py

@@ -1411,6 +1411,11 @@ def div_(self, other, *, rounding_mode=None):
         self.ivy_array = self.div(other, rounding_mode=rounding_mode).ivy_array
         return self
 
+    @with_supported_dtypes({"2.0.1 and below": ("float16", "float32", "float64", "bfloat16")}, "torch")
+    def true_divide_(self, other):
+        self.ivy_array = self.div(other, rounding_mode=None).ivy_array
+        return self
+
     def normal_(self, mean=0, std=1, *, generator=None):
         self.ivy_array = ivy.random_normal(
             mean=mean,

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -9098,6 +9098,47 @@ def test_torch_tensor_div_(
     )
 
 
+# true_divide_
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="true_divide_",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=2,
+        large_abs_safety_factor=2.5,
+        small_abs_safety_factor=2.5,
+        safety_factor_scale="log",
+    ),
+)
+def test_torch_tensor_true_divide_(
+    dtype_and_x,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    assume(not np.any(np.isclose(x[1], 0)))
+
+    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={
+            "other": x[1],
+        },
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
+
+
 # normal_
 @handle_frontend_method(
     class_tree=CLASS_TREE,