NetworkBuilder<N1, E1> castBuilder = cast();
    return new ImmutableNetwork.Builder<>(castBuilder);
  }

  /**
   * Specifies whether the network will allow parallel edges. Attempting to add a parallel edge to a
   * network that does not allow them will throw an {@link UnsupportedOperationException}.
   *
   * <p>The default value is {@code false}.
   */
  @CanIgnoreReturnValue

      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;

  Collection<Object> collection;

  @BeforeExperiment

libcurl4-openssl-dev
libfreetype6-dev
libhdf5-serial-dev
libomp-18-dev
libssl-dev
libtool
libxml2-dev
libxslt1-dev
libzmq3-dev
mlocate
moreutils
openjdk-21-jdk
openjdk-21-jre-headless
openssl
parallel
patchelf
pkg-config
python3-dev
python3-setuptools
rsync
software-properties-common
sudo
swig
unzip
vim
wget
zip

performance limitations.

## Multi-Tenant environments

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the

        network.addNode(gen.nextInt(NODE_POOL_SIZE));
      }
      ArrayList<Integer> nodeList = new ArrayList<>(network.nodes());
      for (int i = 0; i < NUM_EDGES; ++i) {
        // Parallel edges are allowed, so this should always succeed.
        assertThat(
                network.addEdge(
                    getRandomElement(nodeList, gen), getRandomElement(nodeList, gen), new Object()))
            .isTrue();

package cmd

import (
	"context"
	"errors"
	"fmt"
	"io"
	"sync"
	"sync/atomic"

	xioutil "github.com/minio/minio/internal/ioutil"
)

// Reads in parallel from readers.
type parallelReader struct {
	readers       []io.ReaderAt
	orgReaders    []io.ReaderAt
	dataBlocks    int
	offset        int64
	shardSize     int64
	shardFileSize int64
	buf           [][]byte

                        }
                    }
                }
            }
        }
    }
}

// run the parallel ITs
parallel(runITsTasks)

// JENKINS-34376 seems to make it hard to detect the aborted builds
} catch (org.jenkinsci.plugins.workflow.steps.FlowInterruptedException e) {
    echo "[FAILURE-002] FlowInterruptedException ${e}"

import java.util.Collections;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import javax.annotation.CheckForNull;

/**
 * An implementation of {@link NetworkConnections} for undirected networks with parallel edges.
 *
 * @author James Sexton
 * @param <N> Node parameter type
 * @param <E> Edge parameter type
 */
@ElementTypesAreNonnullByDefault
final class UndirectedMultiNetworkConnections<N, E>

fs.s3a.connection.maximum=8192 # Maximum number of concurrent conns
fs.s3a.fast.upload.active.blocks=2048 # Number of parallel uploads
fs.s3a.fast.upload.buffer=disk # Use disk as the buffer for uploads
fs.s3a.fast.upload=true # Turn on fast upload mode
fs.s3a.max.total.tasks=2048 # Maximum number of parallel tasks
fs.s3a.multipart.size=512M # Size of each multipart chunk
fs.s3a.multipart.threshold=512M # Size before using multipart uploads

  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual absl::Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.