// panicking goroutine. Executing a call to recover inside a deferred
// function (but not any function called by it) stops the panicking sequence
// by restoring normal execution and retrieves the error value passed to the
// call of panic. If recover is called outside the deferred function it will
// not stop a panicking sequence. In this case, or when the goroutine is not

  "durations" : [ {
    "testProject" : "archivePerformanceProject",
    "linux" : 167
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.corefeature.ArchiveTreePerformanceTest.packing tar",
  "durations" : [ {
    "testProject" : "archivePerformanceProject",
    "linux" : 159
  } ]
}, {

      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {
    "testId" : "org.gradle.performance.regression.corefeature.ArchiveTreePerformanceTest.packing tar",
    "groups" : [ {
      "testProject" : "archivePerformanceProject",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {

         * initialized in their apps in the past. To do that, we may need to move the references to
         * Unsafe to another class and then ensure that the preinitialization logic doesn't start
         * picking up the new class as part of loading UnsafeComparator!
         */
        return lexicographicalComparatorJavaImpl();
      }
    }

    private LexicographicalComparatorHolder() {}
  }

    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
              unwrapped_handle);
      tensorflow::ImmediateExecutionTensorHandle* result;

                                          void* device_info) {
  TF_SetStatus(status, TF_UNIMPLEMENTED,
               "This custom device does not support packing tensors.");
  return nullptr;
}
}  // namespace

extern "C" {

bool TFE_IsCustomDevice(TFE_Context* ctx, const char* device_name) {
  return tensorflow::unwrap(ctx)->IsCustomDevice(device_name);