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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def ctc_symbol_loss(top_out, targets, model_hparams, vocab_size, weight_fn):
 """Compute the CTC loss."""

  del model_hparams, vocab_size  # unused arg
  logits = top_out
  with tf.name_scope("ctc_loss", values=[logits, targets]):
    # For CTC we assume targets are 1d, [batch, length, 1, 1] here.
    targets_shape = targets.get_shape().as_list()
    assert len(targets_shape) == 4
    assert targets_shape[2] == 1
    assert targets_shape[3] == 1
    targets = tf.squeeze(targets, axis=[2, 3])
    logits = tf.squeeze(logits, axis=[2, 3])
    targets_mask = 1 - tf.to_int32(tf.equal(targets, 0))
    targets_lengths = tf.reduce_sum(targets_mask, axis=1)
    sparse_targets = tf.keras.backend.ctc_label_dense_to_sparse(
        targets, targets_lengths)
    xent = tf.nn.ctc_loss(
        sparse_targets,
        logits,
        targets_lengths,
        time_major=False,
        preprocess_collapse_repeated=False,
        ctc_merge_repeated=False)
    weights = weight_fn(targets)
    return tf.reduce_sum(xent), tf.reduce_sum(weights)