// These fields must satisfy the requirements of Stats' constructor as well as those of the stat
  // methods of this class.
  private long count = 0;
  private double mean = 0.0; // any finite value will do, we only use it to multiply by zero for sum
  private double sumOfSquaresOfDeltas = 0.0;
  private double min = NaN; // any value will do
  private double max = NaN; // any value will do

  /** Adds the given value to the dataset. */

    checkBigIntegerConversion("127.255.255.255", BigInteger.valueOf(Integer.MAX_VALUE));
    checkBigIntegerConversion(
        "255.255.255.254", BigInteger.valueOf(Integer.MAX_VALUE).multiply(BigInteger.valueOf(2)));
    checkBigIntegerConversion(
        "255.255.255.255", BigInteger.ONE.shiftLeft(32).subtract(BigInteger.ONE));
  }

  public void testFromIpv6BigIntegerValid() {

  if (TF_GetCode(status) != TF_OK) return;
  for (int i = 0; i < num_replicas; ++i) {
    (*components)[i].reset(result_handles[i]);
  }
}

TensorHandlePtr Multiply(TFE_Context* context, TFE_TensorHandle* first,
                         TFE_TensorHandle* second, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "Mul", status), TFE_DeleteOp);

    checkBigIntegerConversion("127.255.255.255", BigInteger.valueOf(Integer.MAX_VALUE));
    checkBigIntegerConversion(
        "255.255.255.254", BigInteger.valueOf(Integer.MAX_VALUE).multiply(BigInteger.valueOf(2)));
    checkBigIntegerConversion(
        "255.255.255.255", BigInteger.ONE.shiftLeft(32).subtract(BigInteger.ONE));
  }

  public void testFromIpv6BigIntegerValid() {

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

### Concurrency + Parallelism: Web + Machine Learning

			res[i] = &di
		}
	}
	return res
}

// hasSpaceFor returns whether the disks in `di` have space for and object of a given size.
func hasSpaceFor(di []*DiskInfo, size int64) (bool, error) {
	// We multiply the size by 2 to account for erasure coding.
	size *= 2
	if size < 0 {
		// If no size, assume diskAssumeUnknownSize.
		size = diskAssumeUnknownSize
	}

	var available uint64
	var total uint64

		p.firstProg = prog
	} else {
		p.lastProg.Link = prog
	}
	p.lastProg = prog
	if doLabel {
		p.pc++
		for _, label := range p.pendingLabels {
			if p.labels[label] != nil {
				p.errorf("label %q multiply defined", label)
				return
			}
			p.labels[label] = prog
		}
		p.pendingLabels = p.pendingLabels[0:0]
	}
	prog.Pc = p.pc
	if *flags.Debug {
		fmt.Println(p.lineNum, prog)
	}
	if testOut != nil {

TEST_P(UnifiedCAPI, TestBasicGraphMatMul) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

  // Start a new function / execution context.
  string fn_name = "matrix_multiply";
  TF_ExecutionContext* graph_ctx =
      TF_CreateFunction(fn_name.c_str(), status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  auto* placeholder_t =

			if pctUsed := int(disk.Used * 100 / disk.Total); pctUsed > maxUsedPct {
				maxUsedPct = pctUsed
			}
		}

		// Since we are comparing pools that may have a different number of sets
		// we multiply by the number of sets in the pool.
		// This will compensate for differences in set sizes
		// when choosing destination pool.
		// Different set sizes are already compensated by less disks.
		available *= uint64(nSets[i])

        tf.math.multiply(a, b)

        server.register("multiply", _remote_multiply) ```

    *   Example usage to create client: `python client =
        tf.distribute.experimental.rpc.Client.create("grpc", address) a =
        tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32)
        result = client.multiply(a, b)`

*   `tf.lite`: