Train a model using the input training dataset.

        The API calls the Amazon SageMaker CreateTrainingJob API to start model training.
        The API uses configuration you provided to create the estimator and the
        specified input training data to send the CreatingTrainingJob request to Amazon SageMaker.

        This is a synchronous operation. After the model training successfully completes,
        you can call the ``deploy()`` method to host the model using the Amazon SageMaker hosting services.

        Args:
            inputs (str or dict or sagemaker.session.s3_input): Information about the training data.
                This can be one of three types:

                * (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 as well as the path to the training dataset.
                    See :func:`sagemaker.session.s3_input` for full details.
            wait (bool): Whether the call should wait until the job completes (default: True).
            logs (bool): Whether to show the logs produced by the job.
                Only meaningful when wait is True (default: True).
            job_name (str): Training job name. If not specified, the estimator generates a default job name,
                based on the training image name and current timestamp.

def fit(self, inputs=None, wait=True, logs=True, job_name=None):
        """Train a model using the input training dataset.

        The API calls the Amazon SageMaker CreateTrainingJob API to start model training.
        The API uses configuration you provided to create the estimator and the
        specified input training data to send the CreatingTrainingJob request to Amazon SageMaker.

        This is a synchronous operation. After the model training successfully completes,
        you can call the ``deploy()`` method to host the model using the Amazon SageMaker hosting services.

        Args:
            inputs (str or dict or sagemaker.session.s3_input): Information about the training data.
                This can be one of three types:

                * (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 as well as the path to the training dataset.
                    See :func:`sagemaker.session.s3_input` for full details.
            wait (bool): Whether the call should wait until the job completes (default: True).
            logs (bool): Whether to show the logs produced by the job.
                Only meaningful when wait is True (default: True).
            job_name (str): Training job name. If not specified, the estimator generates a default job name,
                based on the training image name and current timestamp.
        """
        self._prepare_for_training(job_name=job_name)

        self.latest_training_job = _TrainingJob.start_new(self, inputs)
        if wait:
            self.latest_training_job.wait(logs=logs)