feat: add cpu function

ivy/functional/frontends/torch/tensor.py

@@ -633,6 +633,16 @@ def acos_(self):
         self.ivy_array = self.acos().ivy_array
         return self
 
+    @to_ivy_arrays_and_back
+    def to_cpu(self):
+        if (
+            ivy_framework.current_framework_str() == "torch"
+            and ivy_framework.current_device_str() != "cpu"
+        ):
+            return ivy.to_device(self, "cpu")
+        else:
+            return self
+
     def new_tensor(
         self,
         data,

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -52,8 +52,14 @@
     _quantile_helper,
 )
 
+import unittest
+import torch
+from unittest.mock import patch
+from ivy_test import helpers
+from ivy_test.helpers import CLASS_TREE, handle_frontend_methodtry:
+
 try:
-    import torch
+     import torch
 except ImportError:
     torch = SimpleNamespace()
 
@@ -7831,6 +7837,55 @@ def test_torch_index_select(
     )
 
 
+#cpu
+@handle_frontend_method
+class TestTorchInstanceToCPU(unittest.TestCase):(
+        class_tree=CLASS_TREE,
+        init_tree="torch.tensor",
+        method_name="ivy.to_device",
+        dtype_and_x=helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("float"),
+            num_arrays=1,
+            min_value=-1e04,
+            max_value=1e04,
+            allow_inf=False,
+        ),
+    )
+    def test_torch_instance_to_cpu(
+        self,
+        dtype_and_x,
+        frontend,
+        backend_fw,
+        frontend_method_data,
+        init_flags,
+        method_flags,
+    ):
+        input_dtype, x = dtype_and_x
+        with patch("ivy_framework.current_framework_str", return_value="torch"), \
+             patch("ivy_framework.current_device_str", return_value="cpu"):
+            instance = frontend.init_all_as_kwargs_np(
+                input_dtypes=input_dtype, data=x[0]
+            )
+
+            result = frontend.frontend_method_data(
+                instance, method_name="to_cpu", input_dtypes=input_dtype
+            )
+
+            self.assertTrue(torch.all(result.data.cpu() == instance.data.cpu()))
+
+            with patch("ivy_framework.current_framework_str", return_value="numpy"):
+                result = frontend.frontend_method_data(
+                    instance, method_name="to_cpu", input_dtypes=input_dtype
+                )
+
+
+                self.assertEqual(result, instance)
+
+
+if __name__ == "__main__":
+    unittest.main()
+
+
 # int
 @handle_frontend_method(
     class_tree=CLASS_TREE,