<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def fit(self, inputs=None, job_name=None, include_cls_metadata=False, **kwargs):
 """Start a hyperparameter tuning job. Args: inputs: Information about the training data. Please refer to the ``fit()`` method of the associated estimator, as this can take any of the following forms: * (str) - The S3 location where training data is saved. * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for training data, you can specify a dict mapping channel names to strings or :func:`~sagemaker.session.s3_input` objects. * (sagemaker.session.s3_input) - Channel configuration for S3 data sources that can provide additional information about the training dataset. See :func:`sagemaker.session.s3_input` for full details. * (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of Amazon :class:~`Record` objects serialized and stored in S3. For use with an estimator for an Amazon algorithm. * (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of :class:~`sagemaker.amazon.amazon_estimator.RecordSet` objects, where each instance is a different channel of training data. job_name (str):
 Tuning job name. If not specified, the tuner generates a default job name, based on the training image name and current timestamp. include_cls_metadata (bool):
 Whether or not the hyperparameter tuning job should include information about the estimator class (default: False). This information is passed as a hyperparameter, so if the algorithm you are using cannot handle unknown hyperparameters (e.g. an Amazon SageMaker built-in algorithm that does not have a custom estimator in the Python SDK), then set ``include_cls_metadata`` to ``False``. **kwargs: Other arguments needed for training. Please refer to the ``fit()`` method of the associated estimator to see what other arguments are needed. """

        if isinstance(inputs, list) or isinstance(inputs, RecordSet):
            self.estimator._prepare_for_training(inputs, **kwargs)
        else:
            self.estimator._prepare_for_training(job_name)

        self._prepare_for_training(job_name=job_name, include_cls_metadata=include_cls_metadata)
        self.latest_tuning_job = _TuningJob.start_new(self, inputs)