<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def prepare_amazon_algorithm_estimator(estimator, inputs, mini_batch_size=None):
 """ Set up amazon algorithm estimator, adding the required `feature_dim` hyperparameter from training data. Args: estimator (sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase):
 An estimator for a built-in Amazon algorithm to get information from and update. inputs: The training data. * (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. """

    if isinstance(inputs, list):
        for record in inputs:
            if isinstance(record, amazon_estimator.RecordSet) and record.channel == 'train':
                estimator.feature_dim = record.feature_dim
                break
    elif isinstance(inputs, amazon_estimator.RecordSet):
        estimator.feature_dim = inputs.feature_dim
    else:
        raise TypeError('Training data must be represented in RecordSet or list of RecordSets')
    estimator.mini_batch_size = mini_batch_size