//
// Description:
//   Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept
//   a matrix of label probabilities, but rather a single label per row
//   of features.  This label is considered to have probability 1.0 for the
//   given row.
//
//   Inputs are the logits, not probabilities.
Status SparseSoftmaxCrossEntropyWithLogits(AbstractContext* ctx,

		return 0, 0, err
	}

	return accuracyLog, int(br.off), nil
}

// buildFSE builds an FSE decoding table from a list of probabilities.
// The probabilities are in norm. next is scratch space. The number of bits
// in the table is tableBits.
func (r *Reader) buildFSE(off int, norm []int16, table []fseEntry, tableBits int) error {
	tableSize := 1 << tableBits

//
// ProbablyPrime is 100% accurate for inputs less than 2⁶⁴.
// See Menezes et al., Handbook of Applied Cryptography, 1997, pp. 145-149,
// and FIPS 186-4 Appendix F for further discussion of the error probabilities.
//
// ProbablyPrime is not suitable for judging primes that an adversary may
// have crafted to fool the test.
//
// As of Go 1.8, ProbablyPrime(0) is allowed and applies only a Baillie-PSW test.

                    + count
                    + " trials");
          }
        }
      }
    }
  }

  /**
   * Test for avalanche with 2-bit deltas. Most probabilities of output bit(j) differing are well
   * within 50%.
   */
  static void check2BitAvalanche(HashFunction function, int trials, double epsilon) {
    Random rand = new Random(0);
    int keyBits = 32;

	recorder       events.EventRecorder

	serviceHealthServer healthcheck.ServiceHealthServer
	healthzServer       *healthcheck.ProxierHealthServer

	// Since converting probabilities (floats) to strings is expensive
	// and we are using only probabilities in the format of 1/n, we are
	// precomputing some number of those and cache for future reuse.
	precomputedProbabilities []string

        `tf.initializers = tf.keras.initializers` & `tf.optimizers =
        tf.keras.optimizers`.
    *   Updates binary cross entropy logic in Keras when input is probabilities.
        Instead of converting probabilities to logits, we are using the cross
        entropy formula for probabilities.
    *   Added public APIs for `cumsum` and `cumprod` keras backend functions.
    *   Add support for temporal sample weight mode in subclassed models.

  let summary = "Draws samples from a categorical distribution.";

  let description = [{
The generated values will have a categorical distribution based on the `logits`
or unnormalized log-probabilities provided for all classes.
  }];

  let arguments = (ins
    TFL_FpTensor:$logits,
    TFL_I32Tensor:$num_samples,
    DefaultValuedOptionalAttr<I64Attr, "0">:$seed,

- Kube-proxy iptables probabilities are now more granular and will result in better distribution beyond 319 endpoints. ([#83599](https://github.com/kubernetes/kubernetes/pull/83599), [@robscott](https://github.com/robscott))

  }];

  let description = [{
Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept
a matrix of label probabilities, but rather a single label per row
of features.  This label is considered to have probability 1.0 for the
given row.

Inputs are the logits, not probabilities.
  }];

  let arguments = (ins
    Arg<TF_FloatTensor, [{batch_size x num_classes matrix}]>:$features,