def METHOD_NAME(logits):
    """Performs the sparsemax operation when axis=-1."""
    shape_op = tf.shape(logits)
    obs = tf.math.reduce_prod(shape_op[:-1])
    dims = shape_op[-1]
    # In the paper, they call the logits z.
    # The mean(logits) can be substracted from logits to make the algorithm
    # more numerically stable. the instability in this algorithm comes mostly
    # from the z_cumsum. Substacting the mean will cause z_cumsum to be close
    # to zero. However, in practise the numerical instability issues are very
    # minor and substacting the mean causes extra issues with inf and nan
    # input.
    # Reshape to [obs, dims] as it is almost free and means the remanining
    # code doesn't need to worry about the rank.
    z = tf.reshape(logits, [obs, dims])
    # sort z
    z_sorted, _ = tf.nn.top_k(z, k=dims)
    # calculate k(z)
    z_cumsum = tf.math.cumsum(z_sorted, axis=-1)
    k = tf.range(1, tf.cast(dims, logits.dtype) + 1, dtype=logits.dtype)
    z_check = 1 + k * z_sorted > z_cumsum
    # because the z_check vector is always [1,1,...1,0,0,...0] finding the
    # (index + 1) of the last `1` is the same as just summing the number of 1.
    k_z = tf.math.reduce_sum(tf.cast(z_check, tf.int32), axis=-1)
    # calculate tau(z)
    # If there are inf values or all values are -inf, the k_z will be zero,
    # this is mathematically invalid and will also cause the gather_nd to fail.
    # Prevent this issue for now by setting k_z = 1 if k_z = 0, this is then
    # fixed later (see p_safe) by returning p = nan. This results in the same
    # behavior as softmax.
    k_z_safe = tf.math.maximum(k_z, 1)
    indices = tf.stack([tf.range(0, obs), tf.reshape(k_z_safe, [-1]) - 1], axis=1)
    tau_sum = tf.gather_nd(z_cumsum, indices)
    tau_z = (tau_sum - 1) / tf.cast(k_z, logits.dtype)
    # calculate p
    p = tf.math.maximum(tf.cast(0, logits.dtype), z - tf.expand_dims(tau_z, -1))
    # If k_z = 0 or if z = nan, then the input is invalid
    p_safe = tf.where(
        tf.expand_dims(
            tf.math.logical_or(tf.math.equal(k_z, 0), tf.math.is_nan(z_cumsum[:, -1])),
            axis=-1,
        ),
        tf.fill([obs, dims], tf.cast(float("nan"), logits.dtype)),
        p,
    )
    # Reshape back to original size
    p_safe = tf.reshape(p_safe, shape_op)
    return p_safe