add copy_ to Torch frontend (ivy-llc#22343)

ivy/functional/frontends/torch/tensor.py

@@ -375,6 +375,14 @@ def log2_(self):
         self.ivy_array = self.log2().ivy_array
         return self
 
+    @with_unsupported_dtypes({"2.0.1 and below": ("bfloat16", "uint16")}, "torch")
+    def copy_(self, other, non_blocking=False):
+        ivy.utils.assertions.check_one_way_broadcastable(
+            self.ivy_array.shape, torch_frontend.tensor(other).ivy_array.shape
+        )
+        self._ivy_array = torch_frontend.tensor(other).ivy_array
+        return self
+
     @with_unsupported_dtypes({"2.0.1 and below": ("float16",)}, "torch")
     def arccosh(self):
         return torch_frontend.arccosh(self)

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -4706,6 +4706,44 @@ def test_torch_tensor_acos_(
     )
 
 
+# copy_
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="copy_",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=2,
+    ),
+)
+def test_torch_tensor_copy_(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    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,
+    )
+
+
 # asin_
 @handle_frontend_method(
     class_tree=CLASS_TREE,