diff --git a/torch/_deploy.py b/torch/_deploy.py
index 30c022eac879..35e8d4976940 100644
--- a/torch/_deploy.py
+++ b/torch/_deploy.py
@@ -88,7 +88,7 @@ def _load_storages(id, zip_reader, obj_bytes, serialized_storages, serialized_dt
         importer = sys_importer
 
     unpickler = PackageUnpickler(importer, io.BytesIO(obj_bytes))
-    unpickler.persistent_load = persistent_load  # type: ignore[assignment]
+    unpickler.persistent_load = persistent_load  # type: ignore[method-assign]
     result = _deploy_objects[id] = unpickler.load()
     return result
 
diff --git a/torch/_lobpcg.py b/torch/_lobpcg.py
index a5ed5cf8fcfd..6ca1e7294217 100644
--- a/torch/_lobpcg.py
+++ b/torch/_lobpcg.py
@@ -648,7 +648,7 @@ def _lobpcg(
         bparams["ortho_use_drop"] = bparams.get("ortho_use_drop", False)
 
     if not torch.jit.is_scripting():
-        LOBPCG.call_tracker = LOBPCG_call_tracker  # type: ignore[assignment]
+        LOBPCG.call_tracker = LOBPCG_call_tracker  # type: ignore[method-assign]
 
     if len(A.shape) > 2:
         N = int(torch.prod(torch.tensor(A.shape[:-2])))
@@ -672,7 +672,7 @@ def _lobpcg(
             bXret[i] = worker.X[:, :k]
 
         if not torch.jit.is_scripting():
-            LOBPCG.call_tracker = LOBPCG_call_tracker_orig  # type: ignore[assignment]
+            LOBPCG.call_tracker = LOBPCG_call_tracker_orig  # type: ignore[method-assign]
 
         return bE.reshape(A.shape[:-2] + (k,)), bXret.reshape(A.shape[:-2] + (m, k))
 
@@ -684,7 +684,7 @@ def _lobpcg(
     worker.run()
 
     if not torch.jit.is_scripting():
-        LOBPCG.call_tracker = LOBPCG_call_tracker_orig  # type: ignore[assignment]
+        LOBPCG.call_tracker = LOBPCG_call_tracker_orig  # type: ignore[method-assign]
 
     return worker.E[:k], worker.X[:, :k]
 
diff --git a/torch/_refs/__init__.py b/torch/_refs/__init__.py
index d18e24bb4019..22df61b1f171 100644
--- a/torch/_refs/__init__.py
+++ b/torch/_refs/__init__.py
@@ -2161,7 +2161,7 @@ def _reduction(
     computation_dtype, result_dtype = utils.reduction_dtypes(
         a, output_dtype_kind, dtype
     )
-    a = _maybe_convert_to_dtype(a, computation_dtype)  # type: ignore[assignment]
+    a = _maybe_convert_to_dtype(a, computation_dtype)  # type: ignore[method-assign]
     result = prim(a, dims)
     if keepdims:
         output_shape = [a.shape[i] if i not in dims else 1 for i in range(a.ndim)]
@@ -2480,7 +2480,7 @@ def mean(
     nelem = 1 if a.ndim == 0 else reduce(operator.mul, (a.shape[i] for i in dims), 1)
     result = true_divide(result, nelem)
     result_dtype = a.dtype if dtype is None else dtype
-    result = _maybe_convert_to_dtype(result, result_dtype)  # type: ignore[assignment]
+    result = _maybe_convert_to_dtype(result, result_dtype)  # type: ignore[method-assign]
     if out is not None:
         assert isinstance(out, TensorLike)
         out = _maybe_resize_out(out, result.shape)
diff --git a/torch/onnx/symbolic_helper.py b/torch/onnx/symbolic_helper.py
index c8b55c7dec99..fbba99c4ec69 100644
--- a/torch/onnx/symbolic_helper.py
+++ b/torch/onnx/symbolic_helper.py
@@ -287,7 +287,7 @@ def parse_args(*arg_descriptors: _ValueDescriptor):
                 arg_names = [None] * len(args)  # type: ignore[list-item]
                 fn_name = None
             args = [
-                _parse_arg(arg, arg_desc, arg_name, fn_name)  # type: ignore[assignment]
+                _parse_arg(arg, arg_desc, arg_name, fn_name)  # type: ignore[method-assign]
                 for arg, arg_desc, arg_name in zip(args, arg_descriptors, arg_names)
             ]
             # only support _outputs in kwargs
diff --git a/torch/testing/_internal/common_utils.py b/torch/testing/_internal/common_utils.py
index e289f89c55b9..68da3dfcf9b8 100644
--- a/torch/testing/_internal/common_utils.py
+++ b/torch/testing/_internal/common_utils.py
@@ -743,9 +743,8 @@ def prof_func_call(*args, **kwargs):
 def prof_meth_call(*args, **kwargs):
     return prof_callable(meth_call, *args, **kwargs)
 
-# TODO fix when https://github.com/python/mypy/issues/2427 is address
-torch._C.ScriptFunction.__call__ = prof_func_call  # type: ignore[assignment]
-torch._C.ScriptMethod.__call__ = prof_meth_call  # type: ignore[assignment]
+torch._C.ScriptFunction.__call__ = prof_func_call  # type: ignore[method-assign]
+torch._C.ScriptMethod.__call__ = prof_meth_call  # type: ignore[method-assign]
 
 def _get_test_report_path():
     # allow users to override the test file location. We need this