'Get the Tensor expressions from the reader.
Args:
hparams: Hyperparameters object with specgram parameters.
Returns:
A dict of key:tensor pairs. This includes "pitch", "wav", and "key".'

def get_baseline_batch(self, hparams):

    example = self.get_example(hparams.batch_size)
    audio = tf.slice(example['audio'], [0], [64000])
    audio = tf.reshape(audio, [1, 64000])
    pitch = tf.slice(example['pitch'], [0], [1])
    velocity = tf.slice(example['velocity'], [0], [1])
    instrument_source = tf.slice(example['instrument_source'], [0], [1])
    instrument_family = tf.slice(example['instrument_family'], [0], [1])
    qualities = tf.slice(example['qualities'], [0], [10])
    qualities = tf.reshape(qualities, [1, 10])
    hop_length = hparams.hop_length
    n_fft = hparams.n_fft
    if (hop_length and n_fft):
        specgram = utils.tf_specgram(audio, n_fft=n_fft, hop_length=hop_length, mask=hparams.mask, log_mag=hparams.log_mag, re_im=hparams.re_im, dphase=hparams.dphase, mag_only=hparams.mag_only)
        shape = ([1] + SPECGRAM_REGISTRY[(n_fft, hop_length)])
        if hparams.mag_only:
            shape[(-1)] = 1
        specgram = tf.reshape(specgram, shape)
        tf.logging.info('SPECGRAM    BEFORE    PADDING', specgram)
        if hparams.pad:
            num_padding = ((2 ** int(np.ceil((np.log(shape[2]) / np.log(2))))) - shape[2])
            tf.logging.info(('num_pading:    %d' % num_padding))
            specgram = tf.reshape(specgram, shape)
            specgram = tf.pad(specgram, [[0, 0], [0, 0], [0, num_padding], [0, 0]])
            specgram = tf.slice(specgram, [0, 0, 0, 0], [(-1), (shape[1] - 1), (-1), (-1)])
            tf.logging.info('SPECGRAM    AFTER    PADDING', specgram)
    if self.is_training:
        (audio, velocity, pitch, specgram, instrument_source, instrument_family, qualities) = tf.train.shuffle_batch([audio, velocity, pitch, specgram, instrument_source, instrument_family, qualities], batch_size=hparams.batch_size, capacity=(20 * hparams.batch_size), min_after_dequeue=(10 * hparams.batch_size), enqueue_many=True)
    elif (hparams.batch_size > 1):
        (audio, velocity, pitch, specgram, instrument_source, instrument_family, qualities) = tf.train.batch([audio, velocity, pitch, specgram, instrument_source, instrument_family, qualities], batch_size=hparams.batch_size, capacity=(10 * hparams.batch_size), enqueue_many=True)
    audio.set_shape([hparams.batch_size, 64000])
    batch = dict(pitch=pitch, velocity=velocity, audio=audio, instrument_source=instrument_source, instrument_family=instrument_family, qualities=qualities, spectrogram=specgram)
    return batch
