In case, `access` format was used, then `minio_events` would be a list, and the MinIO server would have performed an `RPUSH` to append to the list. A consumer of this list would ideally use `BLPOP` to remove list items from the left-end of the list.

## Publish MinIO events via NATS

Install NATS from [here](http://nats.io/).

### Step 1: Add NATS endpoint to MinIO

//   materialized in the address space of the calling process.
// * int is used as an index into arrays.
// * Deletion functions are safe to call on nullptr.
//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//
// Design decisions made:

	SLL	R5, R6, R7           // 1675a0e1
	SLL.S	R5, R6, R7           // 1675b0e1
	SLL	R5, R7               // 1775a0e1
	SLL.S	R5, R7               // 1775b0e1

// Ops with zero shifts should encode as left shifts
	ADD	R0<<0, R1, R2	     // 002081e0
	ADD	R0>>0, R1, R2	     // 002081e0
	ADD	R0->0, R1, R2	     // 002081e0
	ADD	R0@>0, R1, R2	     // 002081e0
	MOVW	R0<<0(R1), R2        // 002091e7

of s in GF(N) using Fermat's method // (exponentiation modulo P - 2, per Euler's theorem) kInv.Exp(k, c.nMinus2, c.N) } // hashToNat sets e to the left-most bits of hash, according to // FIPS 186-5, Section 6.4.1, point 2 and Section 6.4.2, point 3. func hashToNat[P Point[P]](c *Curve[P], e *bigmod.Nat, hash []byte) { // ECDSA asks us to take the left-most log2(N) bits of hash, and use them as // an integer modulo N. This is the absolute worst of all worlds: we still // have to reduce, because the...

of s in GF(N) using Fermat's method // (exponentiation modulo P - 2, per Euler's theorem) kInv.Exp(k, c.nMinus2, c.N) } // hashToNat sets e to the left-most bits of hash, according to // FIPS 186-5, Section 6.4.1, point 2 and Section 6.4.2, point 3. func hashToNat[P Point[P]](c *Curve[P], e *bigmod.Nat, hash []byte) { // ECDSA asks us to take the left-most log2(N) bits of hash, and use them as // an integer modulo N. This is the absolute worst of all worlds: we still // have to reduce, because the...

    *   Various random number generation improvements:
    *   Add a fast path for default `random_uniform`
    *   `random_seed` documentation improvement.
    *   `RandomBinomial` broadcasts and appends the sample shape to the left
        rather than the right.
    *   Added `tf.random.stateless_binomial`, `tf.random.stateless_gamma`,
        `tf.random.stateless_poisson`
    *   `tf.random.stateless_uniform` now supports unbounded sampling of `int`