public static String createMavenVersionString(Properties buildProperties) {
        String timestamp = reduce(buildProperties.getProperty("timestamp"));
        String version = reduce(buildProperties.getProperty(BUILD_VERSION_PROPERTY));
        String rev = reduce(buildProperties.getProperty("buildNumber"));
        String distributionName = reduce(buildProperties.getProperty("distributionName"));

        String msg = distributionName + " ";

      }
    },
    REDUCE_LAST {
      @Override
      Object operate(Stream<?> stream) {
        return stream.reduce((a, b) -> b);
      }
    },
    REDUCE_LAST_PARALLEL {
      @Override
      Object operate(Stream<?> stream) {
        return stream.parallel().reduce((a, b) -> b);
      }
    };

    abstract Object operate(Stream<?> stream);
  }

  @Param private Operation operation;

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

				// Number of probes.
				// ~ cat /proc/sys/net/ipv4/tcp_keepalive_probes (defaults to 9, we reduce it to 5)
				_ = syscall.SetsockoptInt(fd, syscall.IPPROTO_TCP, syscall.TCP_KEEPCNT, 5)

				// Wait time after successful probe in seconds.
				// ~ cat /proc/sys/net/ipv4/tcp_keepalive_intvl (defaults to 75 secs, we reduce it to 15 secs)
				_ = syscall.SetsockoptInt(fd, syscall.IPPROTO_TCP, syscall.TCP_KEEPINTVL, 15)
			}

  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();
    return absl::OkStatus();
  }

  // Else, reduce sum the output to get a scalar

  // Will sum all dimensions, so get a Tensor containing [0,...,num_dims_out-1].
  AbstractTensorHandlePtr sum_dims;
  {
    vector<int32_t> vals(num_dims_out);

net.ipv4.tcp_max_syn_backlog=16384
net.core.wmem_max=4194304
net.core.rmem_max=4194304
net.core.rmem_default=4194304
net.core.wmem_default=4194304
net.ipv4.tcp_rmem="4096 87380 4194304"
net.ipv4.tcp_wmem="4096 65536 4194304"

# Reduce CPU utilization
net.ipv4.tcp_timestamps=0

# Increase throughput
net.ipv4.tcp_sack=1

# Low latency mode for TCP
net.ipv4.tcp_low_latency=1

# The following variable is used to tell the kernel how 

			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)

    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

   * 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 {

                                }
                            }
                        } finally {
                            deleteDir() // clean up after ourselves to reduce disk space
                        }
                    }
                }
            }
        }
    }
}

// run the parallel ITs
parallel(runITsTasks)