need = 1 // to ensure we read at least one byte for every Read
			tokens = b
		}
	} else { // all tokens go towards payload
		need = int(math.Min(float64(b), float64(need)))
		tokens = need
	}
	// reduce tokens requested according to availability
	av := int(r.throttle.Tokens())
	if av < tokens && av > 0 {
		tokens = av
		need = int(math.Min(float64(tokens), float64(need)))
	}
	err = r.throttle.WaitN(r.ctx, tokens)

 *
 * <p>This interface is now a legacy type. Use {@code java.util.function.Supplier} (or the
 * appropriate primitive specialization such as {@code IntSupplier}) instead whenever possible.
 * Otherwise, at least reduce <i>explicit</i> dependencies on this type by using lambda expressions
 * or method references instead of classes, leaving your code easier to migrate in the future.
 *

      return DummyProxy.this;
    }

    @Override
    public String toString() {
      return "Dummy proxy for " + interfaceType;
    }

    // Since type variables aren't serializable, reduce the type down to raw type before
    // serialization.
    private Object writeReplace() {
      return new DummyHandler(TypeToken.of(interfaceType.getRawType()));
    }
  }

 *
 * <p>This interface is now a legacy type. Use {@code java.util.function.Predicate} (or the
 * appropriate primitive specialization such as {@code IntPredicate}) instead whenever possible.
 * Otherwise, at least reduce <i>explicit</i> dependencies on this type by using lambda expressions
 * or method references instead of classes, leaving your code easier to migrate in the future.
 *

		}
	}
}

// Test for reduceErrs, reduceErr reduces collection
// of errors into a single maximal error with in the list.
func TestReduceErrs(t *testing.T) {
	canceledErrs := make([]error, 0, 5)
	for i := 0; i < 5; i++ {
		canceledErrs = append(canceledErrs, fmt.Errorf("error %d: %w", i, context.Canceled))
	}
	// List all of all test cases to validate various cases of reduce errors.
	testCases := []struct {
		errs        []error

and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.
Typically, enabling compression on spinning disk systems
will increase speed when the content can be compressed.

## Get Started

### 1. Prerequisites

    R apply(double from, long index);
  }

  /**
   * Returns the last element of the specified stream, or {@link java.util.Optional#empty} if the
   * stream is empty.
   *
   * <p>Equivalent to {@code stream.reduce((a, b) -> b)}, but may perform significantly better. This
   * method's runtime will be between O(log n) and O(n), performing better on <a

 *
 * <p>This interface is now a legacy type. Use {@code java.util.function.Predicate} (or the
 * appropriate primitive specialization such as {@code IntPredicate}) instead whenever possible.
 * Otherwise, at least reduce <i>explicit</i> dependencies on this type by using lambda expressions
 * or method references instead of classes, leaving your code easier to migrate in the future.
 *

/**
 * One or more variables that together maintain an initially zero {@code long} sum. When updates
 * (method {@link #add}) are contended across threads, the set of variables may grow dynamically to
 * reduce contention. Method {@link #sum} (or, equivalently, {@link #longValue}) returns the current
 * total combined across the variables maintaining the sum.
 *

   * This test creates a long random sequence of inputs, then a lot of differently configured sinks
   * process it; all should produce the same answer, the only difference should be the number of
   * process()/processRemaining() invocations, due to alignment.
   */
  @AndroidIncompatible // slow. TODO(cpovirk): Maybe just reduce iterations under Android.
  public void testExhaustive() throws Exception {