|---| | [ View source on GitHub](https://github.com/tensorflow/models/blob/v2.20.0/official/core/config_definitions.py#L288-L304) | Config passed to task. Inherits From: [`Config`](https://www.tensorflow.org/api_docs/python/tfm/hyperparams/Config), [`ParamsDict`](https://www.tensorflow.org/api_docs/python/tfm/hyperparams/ParamsDict) tfm.core.config_definitions.TaskConfig( default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None, restrictions: dataclasses.InitVar[Optional[List[str]]] = None, init_checkpoint: str = '', model: Optional[https://www.tensorflow.org/api_docs/python/tfm/hyperparams/Config] = None, train_data: https://www.tensorflow.org/api_docs/python/tfm/core/config_definitions/DataConfig = dataclasses.field(default_factory=DataConfig), validation_data: https://www.tensorflow.org/api_docs/python/tfm/core/config_definitions/DataConfig = dataclasses.field(default_factory=DataConfig), name: Optional[str] = None, differential_privacy_config: Optional[https://www.tensorflow.org/api_docs/python/tfm/core/base_task/DifferentialPrivacyConfig] = None, allow_image_summary: bool = False )


| ## Args || |---|---| | `default_params` | a Python dict or another ParamsDict object including the default parameters to initialize. | | `restrictions` | a list of strings, which define a list of restrictions to ensure the consistency of different parameters internally. Each restriction string is defined as a binary relation with a set of operators, including {'==', '!=', '\<', '\<=', '\>', '\>='}. |



| ## Attributes || |---|---| | `BUILDER` |

| | `default_params` | Dataclass field | | `restrictions` | Dataclass field | | `init_checkpoint` | Dataclass field | | `model` | Dataclass field | | `train_data` | Dataclass field | | `validation_data` | Dataclass field | | `name` | Dataclass field | | `differential_privacy_config` | Dataclass field | | `allow_image_summary` | Dataclass field |
## Methods ### `as_dict` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L315-L324) as_dict() Returns a dict representation of params_dict.ParamsDict. For the nested params_dict.ParamsDict, a nested dict will be returned. ### `from_args` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L349-L358) @classmethod from_args( *args, **kwargs ) Builds a config from the given list of arguments. ### `from_json` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L344-L347) @classmethod from_json( file_path: str ) Wrapper for `from_yaml`. ### `from_yaml` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L335-L342) @classmethod from_yaml( file_path: str ) ### `get` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L142-L144) get( key, value=None ) Accesses through built-in dictionary get method. ### `lock` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L204-L206) lock() Makes the ParamsDict immutable. ### `override` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L165-L181) override( override_params, is_strict=True ) Override the ParamsDict with a set of given params.


| Args || |---|---| | `override_params` | a dict or a ParamsDict specifying the parameters to be overridden. | | `is_strict` | a boolean specifying whether override is strict or not. If True, keys in `override_params` must be present in the ParamsDict. If False, keys in `override_params` can be different from what is currently defined in the ParamsDict. In this case, the ParamsDict will be extended to include the new keys. |
### `replace` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L326-L333) replace( **kwargs ) Overrides/returns a unlocked copy with the current config unchanged. ### `validate` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L222-L329) validate() Validate the parameters consistency based on the restrictions. This method validates the internal consistency using the pre-defined list of restrictions. A restriction is defined as a string which specifies a binary operation. The supported binary operations are {'==', '!=', '\<', '\<=', '\>', '\>='}. Note that the meaning of these operators are consistent with the underlying Python immplementation. Users should make sure the define restrictions on their type make sense. For example, for a ParamsDict like the following a: a1: 1 a2: 2 b: bb: bb1: 10 bb2: 20 ccc: a1: 1 a3: 3 one can define two restrictions like this \['a.a1 == b.ccc.a1', 'a.a2 \<= b.bb.bb2'\]

| What it enforces are || |---|---| |
- a.a1 = 1 == b.ccc.a1 = 1 - a.a2 = 2 \<= b.bb.bb2 = 20 ||



| Raises || |---|---| | `KeyError` | if any of the following happens (1) any of parameters in any of restrictions is not defined in ParamsDict, (2) any inconsistency violating the restriction is found. | | `ValueError` | if the restriction defined in the string is not supported. |
### `__contains__` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L138-L140) __contains__( key ) Implements the membership test operator. ### `__eq__` __eq__( other ) Return self==value.


| ## Class Variables || |---|---| | IMMUTABLE_TYPES | `(, , , , )` | | RESERVED_ATTR | `['_locked', '_restrictions']` | | SEQUENCE_TYPES | `(, )` | | allow_image_summary | `False` | | default_params | `None` | | differential_privacy_config | `None` | | init_checkpoint | `''` | | model | `None` | | name | `None` | | restrictions | `None` |