basic_pitch/data/tf_example_deserialization.py [45:66]:
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    batch_size: int,
    validation_steps: int,
    datasets_to_use: List[str],
    dataset_sampling_frequency: np.ndarray,
) -> Tuple[tf.data.Dataset, tf.data.Dataset]:
    """
    Return a training and a testing dataset.

    Args:
        datasets_base_path: path to tfrecords for input data
        training_shuffle_buffer_size: size of shuffle buffer (only for training set)
        batch_size: batch size for training and validation
        validation_steps: number of batches to use for validation
        datasets_to_use: the underlying datasets to use for creating training and validation sets e.g. guitarset
        dataset_sampling_frequency: distribution weighting vector corresponding to datasets determining how they
            are sampled from during training / validation dataset creation.

    Returns:
        training and validation datasets derived from the underlying tfrecord data
    """
    assert batch_size > 0
    assert validation_steps is not None and validation_steps > 0
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basic_pitch/data/tf_example_deserialization.py [113:134]:
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    batch_size: int,
    validation_steps: int,
    datasets_to_use: List[str],
    dataset_sampling_frequency: np.ndarray,
) -> Tuple[tf.data.Dataset, tf.data.Dataset]:
    """
    Return a training and a testing dataset for visualization

    Args:
        datasets_base_path: path to tfrecord datasets for input data
        batch_size: batch size for training and validation
        validation_steps: number of batches to use for validation
        datasets_to_use: the underlying datasets to use for creating training and validation sets e.g. guitarset
        dataset_sampling_frequency: distribution weighting vector corresponding to datasets determining how they
            are sampled from during training / validation dataset creation.

    Returns:
        training and validation datasets derived from the underlying tfrecord data
    """

    assert batch_size > 0
    assert validation_steps is not None and validation_steps > 0
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