<distribution>repo</distribution>
    </license>
  </licenses>

  <developers>
    <!-- Sonatype requires that we list someone here. Guava has many contributors: https://github.com/google/guava/graphs/contributors -->
    <developer>
      <id>cpovirk</id>
      <name>Chris Povirk</name>
      <email>******@****.***</email>
    </developer>
  </developers>

  <scm>

/*!
 * Timepicker Component for Twitter Bootstrap
 *
 * Copyright 2013 Joris de Wit
 *
 * Contributors https://github.com/jdewit/bootstrap-timepicker/graphs/contributors
 *
 * For the full copyright and license information, please view the LICENSE
 * file that was distributed with this source code.

    assertThat(graph.nodeOrder().comparator()).isEqualTo(Ordering.natural());
    assertThat(graph.nodes()).containsExactly(node2, node4, node6, node8).inOrder();
  }

  private static void addNodes(MutableGraph<Integer> graph) {
    graph.addNode(3);
    graph.addNode(1);
    graph.addNode(4);
  }

  private static void addEdges(MutableNetwork<Integer, String> network) {
    network.addEdge(3, 1, "i");

import org.eclipse.aether.util.graph.manager.ClassicDependencyManager;
import org.eclipse.aether.util.graph.manager.TransitiveDependencyManager;
import org.eclipse.aether.util.graph.selector.AndDependencySelector;
import org.eclipse.aether.util.graph.selector.ExclusionDependencySelector;
import org.eclipse.aether.util.graph.transformer.ChainedDependencyGraphTransformer;
import org.eclipse.aether.util.graph.transformer.ConfigurableVersionSelector;

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing...

            PerformanceTest(
                model,
                stage,
                flameGraphGenerationBuildSpec,
                description =
                    "Flame graphs with $profiler for ${performanceScenario.scenario.scenario} | ${performanceScenario.testProject} " +
                        "on ${os.asName()} (bucket $bucketIndex)",
                performanceSubProject = "performance",

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

  // Maps from name of an operation to the Node* in 'graph'.
  std::unordered_map<std::string, tensorflow::Node*> name_map TF_GUARDED_BY(mu);

  // The keys of this map are all the active sessions using this graph. Each

IMPORTANT: The 2.0 version of the profiler doesn't seem to be with compatible
with JMH as of 2021-04-30.

The async profiler is neat because it does not suffer from the safepoint
bias problem. And because it makes pretty flame graphs!

Let user processes read performance stuff:
```
sudo bash
echo 0 > /proc/sys/kernel/kptr_restrict
echo 1 > /proc/sys/kernel/perf_event_paranoid
exit
```

 * limitations under the License.
 */

package com.google.common.graph;

import static com.google.common.base.Preconditions.checkArgument;
import static com.google.common.base.Preconditions.checkNotNull;
import static com.google.common.base.Preconditions.checkState;
import static com.google.common.graph.GraphConstants.PARALLEL_EDGES_NOT_ALLOWED;
import static com.google.common.graph.GraphConstants.REUSING_EDGE;

{% endif %}

{% endfor %}

</div>

There are hundreds of other contributors, you can see them all in the [FastAPI GitHub Contributors page](https://github.com/fastapi/fastapi/graphs/contributors). 👷

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