Corrected the docstring of TuningConfig

neural_compressor/common/base_tuning.py

@@ -14,12 +14,11 @@
 
 
 import copy
-import inspect
 import uuid
 from typing import Any, Callable, Dict, Generator, Iterator, List, Optional, Sized, Tuple, Union
 
 from neural_compressor.common import Logger
-from neural_compressor.common.base_config import BaseConfig, ComposableConfig
+from neural_compressor.common.base_config import BaseConfig
 from neural_compressor.common.utils import TuningLogger
 
 logger = Logger().get_logger()
@@ -227,19 +226,11 @@ def __iter__(self) -> Generator[BaseConfig, Any, None]:
 
 
 class TuningConfig:
-    """Base Class for Tuning Criterion.
-
-    Args:
-        config_set: quantization configs. Default value is empty.
-            A single config or a list of configs. More details can
-            be found in the `from_fwk_configs`of `ConfigSet` class.
-        max_trials: Max tuning times. Default value is 100. Combine with timeout field to decide when to exit.
-        tolerable_loss: This float indicates how much metric loss we can accept. \
-            The metric loss is relative, it can be both positive and negative. Default is 0.01.
+    """Config for auto tuning pipeline.
 
     Examples:
         # TODO: to refine it
-        from neural_compressor import TuningConfig
+        from neural_compressor.torch.quantization import TuningConfig
         tune_config = TuningConfig(
             config_set=[config1, config2, ...],
             max_trials=3,
@@ -264,13 +255,25 @@ class TuningConfig:
     """
 
     def __init__(
-        self, config_set=None, max_trials=100, sampler: Sampler = default_sampler, tolerable_loss=0.01
-    ) -> None:
-        """Init a TuneCriterion object."""
+        self,
+        config_set: Union[BaseConfig, List[BaseConfig]] = None,
+        sampler: Sampler = default_sampler,
+        tolerable_loss=0.01,
+        max_trials=100,
+    ):
+        """Initial a TuningConfig.
+
+        Args:
+            config_set: A single config or a list of configs. Defaults to None.
+            sampler: tuning sampler that decide the trials order. Defaults to default_sampler.
+            tolerable_loss: This float indicates how much metric loss we can accept.
+                The metric loss is relative, it can be both positive and negative. Default is 0.01.
+            max_trials: Max tuning times. Combine with `tolerable_loss` field to decide when to stop. Default is 100.
+        """
         self.config_set = config_set
-        self.max_trials = max_trials
         self.sampler = sampler
         self.tolerable_loss = tolerable_loss
+        self.max_trials = max_trials
 
 
 class _TrialRecord: