the dimension `seq_dim`.

The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`,
and `seq_lengths` must be a vector of length `input.dims[batch_dim]`.

The output slice `i` along dimension `batch_dim` is then given by input
slice `i`, with the first `seq_lengths[i]` slices along dimension
`seq_dim` reversed.

For example:

```

`tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`:
`reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`
becomes `axis` * `tf.reverse_sequence`: `batch_dim` becomes `batch_axis`,
`seq_dim` becomes `seq_axis` * `tf.sparse_concat`: `concat_dim` becomes `axis` *
`tf.sparse_reduce_sum`: `reduction_axes` becomes `axis` *
`tf.sparse_reduce_sum_sparse`: `reduction_axes` becomes `axis` *