Remove Standardize.load_state_dict

Summary: This was a temporary wrokaround for backwards. compatibility, added in #1875

Reviewed By: Balandat

Differential Revision: D56801096

fbshipit-source-id: c867fe2ca2c65d5f4c6ca9a09fd4798452767cc0

Author: saitcakmak

botorch/models/transforms/outcome.py

@@ -22,11 +22,9 @@
 
 from __future__ import annotations
 
-import warnings
-
 from abc import ABC, abstractmethod
 from collections import OrderedDict
-from typing import Any, List, Mapping, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import torch
 from botorch.models.transforms.utils import (
@@ -256,19 +254,6 @@ def __init__(
         self._batch_shape = batch_shape
         self._min_stdv = min_stdv
 
-    def load_state_dict(
-        self, state_dict: Mapping[str, Any], strict: bool = True
-    ) -> None:
-        r"""Custom logic for loading the state dict."""
-        if "_is_trained" not in state_dict:
-            warnings.warn(
-                "Key '_is_trained' not found in state_dict. Setting to True. "
-                "In a future release, this will result in an error.",
-                DeprecationWarning,
-            )
-            state_dict = {**state_dict, "_is_trained": torch.tensor(True)}
-        super().load_state_dict(state_dict, strict=strict)
-
     def forward(
         self, Y: Tensor, Yvar: Optional[Tensor] = None
     ) -> Tuple[Tensor, Optional[Tensor]]:

test/models/transforms/test_outcome.py

@@ -355,13 +355,6 @@ def test_standardize_state_dict(self):
                 self.assertFalse(new_transform._is_trained)
                 new_transform.load_state_dict(state_dict)
                 self.assertTrue(new_transform._is_trained)
-                # test deprecation error when loading state dict without _is_trained
-                state_dict.pop("_is_trained")
-                with self.assertWarnsRegex(
-                    DeprecationWarning,
-                    "Key '_is_trained' not found in state_dict. Setting to True.",
-                ):
-                    new_transform.load_state_dict(state_dict)
 
     def test_log(self):
         ms = (1, 2)