text(
        "profiler",
        defaultProfiler,
        display = ParameterDisplay.PROMPT,
        allowEmpty = false,
        description =
            "Command line option for the performance test task to enable profiling. " +
                "For example `async-profiler`, `async-profiler-heap`, `async-profiler-wall`, `async-profiler-all` or `jfr`. Use `none` for benchmarking only.",
    )
}

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

        )

        // TODO: Gradle profiler should use the bundled tooling API.
        //   This should actually be handled by conflict resolution, though it doesn't seem to work.
        //   See https://github.com/gradle/gradle/issues/12002.
        applyRule<DependencyRemovalByNameRule>(
            "org.gradle.profiler:gradle-profiler",
            setOf("gradle-tooling-api")
        )
    }
}

        <include>mvnsh</include>
        <include>mvnup</include>
        <include>mvnDebug</include>
        <include>mvnencDebug</include>
        <!-- This is so that CI systems can periodically run the profiler -->
        <include>mvnyjp</include>
      </includes>
      <lineEnding>unix</lineEnding>
      <fileMode>0755</fileMode>
    </fileSet>
    <fileSet>
      <directory>src/assembly/maven/conf</directory>

        "//tensorflow/core/platform:stringpiece",
        "//tensorflow/core/platform:types",
        "//tensorflow/core/profiler/internal:print_model_analysis",
        "//tensorflow/core/profiler/lib:traceme",
        "//tensorflow/core/profiler/rpc/client:profiler_client_impl",
        "//tensorflow/core/profiler/rpc:profiler_server_impl",
        "//tensorflow/core/util:managed_stack_trace",
        "//tensorflow/core:all_kernels",

            "//tensorflow/core/common_runtime/eager:execute",
            "//tensorflow/core/common_runtime/eager:placement_utils",
            "//tensorflow/core/common_runtime/eager:tensor_handle",
            "//tensorflow/core/profiler/lib:traceme",
            "@com_google_absl//absl/algorithm:container",
            "@com_google_absl//absl/types:span",
            "@com_google_absl//absl/types:variant",
        ],
    }) + [

    analyzing input pipeline performance with TF Profiler.

*   [`tf.distribute.TPUStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/TPUStrategy)
    is now a stable API and no longer considered experimental for TensorFlow.
    (earlier `tf.distribute.experimental.TPUStrategy`).

*   [TF Profiler](https://www.tensorflow.org/guide/profiler) introduces two new
    tools: a memory profiler to visualize your model’s memory usage over time

    }

    private static boolean isEmpty(Profile profile) {
        return profile.getActivation() == null
                && profile.getBuild() == null
                && profile.getDependencies().isEmpty()
                && (profile.getDependencyManagement() == null
                        || profile.getDependencyManagement().getDependencies().isEmpty())
                && profile.getDistributionManagement() == null

                .map(Profile::getId);
        final Stream<String> settingsProfiles =
                session.getSettings().getProfiles().stream().map(org.apache.maven.settings.Profile::getId);
        final Stream<String> superPomProfiles = superPomModels.values().stream()
                .flatMap(p -> p.getProfiles().stream())
                .map(Profile::getId);

              </execution>
            </executions>
          </plugin>
        </plugins>
      </build>
    </profile>
    <profile>
      <id>versionlessMavenDist</id>
      <build>
        <finalName>${project.artifactId}</finalName>
      </build>
    </profile>
  </profiles>