//  [python]
def sample_sequence(*, hparams, length, min_length=0, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, top_p=0.0):
    if start_token is None:
        assert context is not None, 'Specify exactly one of start_token and context!'
    else:
        assert context is None, 'Specify exactly one of start_token and context!'
        context = tf.fill([batch_size, 1], start_token)

    def step(hparams, tokens, past=None):
        lm_output = model.model(hparams=hparams, X=tokens, past=past, reuse=tf.AUTO_REUSE)

        logits = lm_output['logits'][:, :, :hparams.n_vocab]
        presents = lm_output['present']
        presents.set_shape(model.past_shape(hparams=hparams, batch_size=batch_size))
        return {
            'logits': logits,
            'presents': presents,
        }

    with tf.name_scope('sample_sequence'):
        # Don't feed the last context token -- leave that to the loop below
        # TODO: Would be slightly faster if we called step on the entire context,
        # rather than leaving the last token transformer calculation to the while loop.
        context_output = step(hparams, context[:, :-1])

        def body1(past, prev, output):
            next_outputs = step(hparams, prev[:, tf.newaxis], past=past)
            logits = next_outputs['logits'][:, -1, :]  / tf.to_float(temperature)
            if top_p > 0.0:
                logits = top_p_logits(logits, p=top_p)
            else:
                logits = top_k_logits(logits, k=top_k)
            samples = tf.random.categorical(logits[:, :-1], num_samples=1, dtype=tf.int32)
            return [
                tf.concat([past, next_outputs['presents']], axis=-2),
                tf.squeeze(samples, axis=[1]),
                tf.concat([output, samples], axis=1),
            ]

        def body2(past, prev, output):
            next_outputs = step(hparams, prev[:, tf.newaxis], past=past)
            logits = next_outputs['logits'][:, -1, :]  / tf.to_float(temperature)
            if top_p > 0.0:
                logits = top_p_logits(logits, p=top_p)
            else:
                logits = top_k_logits(logits, k=top_k)
            samples = tf.random.categorical(logits, num_samples=1, dtype=tf.int32)
            return [
                tf.concat([past, next_outputs['presents']], axis=-2),
                tf.squeeze(samples, axis=[1]),
                tf.concat([output, samples], axis=1),
            ]

        def cond(*args):
            return True

        if min_length > 0:
            _, _, context = tf.while_loop(
                cond=cond, body=body1,
                maximum_iterations=min_length,
                loop_vars=[
                    context_output['presents'],
                    context[:, -1],
                    context,
                ],
                shape_invariants=[
                    tf.TensorShape(model.past_shape(hparams=hparams, batch_size=batch_size)),
                    tf.TensorShape([batch_size]),
                    tf.TensorShape([batch_size, None]),
                ],
                back_prop=False,
            )

        _, _, tokens = tf.while_loop(
            cond=cond, body=body2,
            maximum_iterations=length-min_length,
            loop_vars=[
                context_output['presents'],
                context[:, -1],
                context,
            ],
            shape_invariants=[
                tf.TensorShape(model.past_shape(hparams=hparams, batch_size=batch_size)),
                tf.TensorShape([batch_size]),
                tf.TensorShape([batch_size, None]),
            ],
            back_prop=False,
        )

        return tokens