"//conditions:default": [],
    }),
    tags = [
        "no_cuda_asan",  # TODO(b/181771536)
        "no_windows",  # TODO(b/155444728)
    ],
    # We must ensure that the dependencies can be dynamically linked since
    # the shared library must be able to use core:framework.
    deps = [
        ":c_api",
        ":c_api_internal",
        ":c_test_util",
        ":test_op_kernel",
        ":tf_buffer",

    - [pilot](pilot/). This directory
contains platform-specific code to populate the
[abstract service model](https://istio.io/docs/concepts/traffic-management/#pilot), dynamically reconfigure the proxies
when the application topology changes, as well as translate
[routing rules](https://istio.io/latest/docs/reference/config/networking/) into proxy specific configuration.

    val tomlj = "org.tomlj:tomlj"
    val trove4j = "org.jetbrains.intellij.deps:trove4j"
    val xbeanReflect = "org.apache.xbean:xbean-reflect"
    val xmlApis = "xml-apis:xml-apis"

    // Compile only dependencies (dynamically downloaded if needed)
    val maven3Compat = "org.apache.maven:maven-compat"
    val maven3PluginApi = "org.apache.maven:maven-plugin-api"
    val zinc = "org.scala-sbt:zinc_2.13"

    // Test classpath only libraries

This means that a CUDA target was probably included via an is_static condition,
used when targeting platforms like Windows where we build statically instead
of dynamically. Here's the output from bazel query:
EOF
  cat $BATS_TEST_TMPDIR/out
  [[ ! -s $BATS_TEST_TMPDIR/out ]]
}

@test "All tensorflow.org/code links point to real files" {

#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     dbg:              Build with debug info
#
# TF version options;
#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA

    public boolean isCancelled() {
      return delegate.isCancelled();
    }
  }

  /**
   * A {@link Scheduler} that provides a convenient way for the {@link AbstractScheduledService} to
   * use a dynamically changing schedule. After every execution of the task, assuming it hasn't been
   * cancelled, the {@link #getNextSchedule} method will be called.
   *
   * @author Luke Sandberg
   * @since 11.0
   */

    FluentIterable<Integer> result = FluentIterable.concat(input);
    assertEquals(asList(1, 4), newArrayList(result));

    // Now change the inputs and see result dynamically change as well

    list1.add(2);
    List<Integer> list3 = newArrayList(3);
    input.add(1, list3);

    assertEquals(asList(1, 2, 3, 4), newArrayList(result));

	writeResponse(w, err.HTTPStatusCode, encodedErrorResponse, mimeJSON)
}

// writeCustomErrorResponseJSON - similar to writeErrorResponseJSON,
// but accepts the error message directly (this allows messages to be
// dynamically generated.)
func writeCustomErrorResponseJSON(ctx context.Context, w http.ResponseWriter, err APIError,
	errBody string, reqURL *url.URL,
) {
	reqInfo := logger.GetReqInfo(ctx)
	errorResponse := APIErrorResponse{

    List<List<Integer>> input = newArrayList(list1, list2);

    Iterable<Integer> result = Iterables.concat(input);
    assertEquals(asList(1, 4), newArrayList(result));

    // Now change the inputs and see result dynamically change as well

    list1.add(2);
    List<Integer> list3 = newArrayList(3);
    input.add(1, list3);

    assertEquals(asList(1, 2, 3, 4), newArrayList(result));

- Updated the feature MultiCIDRServiceAllocator to beta (disabled by default). Users need to enable the feature gate and the networking v1beta1 group to be able to use this new feature, that allows to dynamically reconfigure Service CIDR ranges. ([#125021](https://github.com/kubernetes/kubernetes/pull/125021), [@aojea](https://github.com/aojea)) [SIG API Machinery, Apps, CLI, Etcd, Instrumentation, Network and Testing]