Added true_divide method to the PyTorch frontend

ivy/functional/frontends/torch/tensor.py

@@ -1981,6 +1981,12 @@ def quantile(self, q, dim=None, keepdim=False, *, interpolation="linear", out=No
             self, q, dim=dim, keepdim=keepdim, interpolation=interpolation, out=out
         )
 
+    @with_supported_dtypes(
+        {"2.1.2 and below": ("float16", "float32", "float64", "bfloat16")}, "torch"
+    )
+    def true_divide(self, other):
+        return torch_frontend.div(self, other, rounding_mode=None)
+
     @with_unsupported_dtypes(
         {
             "2.1.2 and below": (

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -13096,6 +13096,47 @@ def test_torch_tensor_split(
     )
 
 
+# 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,
+    )
+
+
 @handle_frontend_method(
     class_tree=CLASS_TREE,
     init_tree="torch.tensor",