refactor(normalization): Remove unused state dict transformation methods and streamline imports

- Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process.
- Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging.
- Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation.
- Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality.

src/lerobot/processor/normalize_processor.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
+
 from collections.abc import Mapping
+from copy import deepcopy
 from dataclasses import dataclass, field
 from typing import Any
 
@@ -10,7 +12,7 @@ from torch import Tensor
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.processor.pipeline import EnvTransition, ProcessorStepRegistry, TransitionKey
+from lerobot.processor.pipeline import EnvTransition, ProcessorStepRegistry, TransitionKey, RobotProcessor
 
 
 def _convert_stats_to_tensors(stats: dict[str, dict[str, Any]]) -> dict[str, dict[str, Tensor]]:
@@ -402,3 +404,13 @@ class UnnormalizerProcessor:
 
     def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
         return features
+
+
+def hotswap_stats(robot_processor: RobotProcessor, stats: dict[str, dict[str, Any]]) -> RobotProcessor:
+    robot_processor = deepcopy(robot_processor)
+    for step in robot_processor.steps:
+        if isinstance(step, NormalizerProcessor) or isinstance(step, UnnormalizerProcessor):
+            step: NormalizerProcessor | UnnormalizerProcessor
+            step.stats = stats
+            step._tensor_stats = _convert_stats_to_tensors(stats)
+    return robot_processor