#
#   Important for patching:
#
#   (2) This script targets any POSIX shell, so it avoids extensions provided
#       by Bash, Ksh, etc; in particular arrays are avoided.
#
#       The "traditional" practice of packing multiple parameters into a
#       space-separated string is a well documented source of bugs and security
#       problems, so this is (mostly) avoided, by progressively accumulating

#
#   Important for patching:
#
#   (2) This script targets any POSIX shell, so it avoids extensions provided
#       by Bash, Ksh, etc; in particular arrays are avoided.
#
#       The "traditional" practice of packing multiple parameters into a
#       space-separated string is a well documented source of bugs and security
#       problems, so this is (mostly) avoided, by progressively accumulating

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

  "durations" : [ {
    "testProject" : "archivePerformanceProject",
    "linux" : 159
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.corefeature.ArchiveTreePerformanceTest.packing tar",
  "durations" : [ {
    "testProject" : "archivePerformanceProject",
    "linux" : 184
  } ]
}, {

    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);

nfiguration/assign-pod-node/). Node affinity/anti-affinity allow you to specify rules for restricting which node(s) a pod can schedule onto, based on the labels on the node. Pod affinity/anti-affinity allow you to specify rules for spreading and packing pods relative to one another, across arbitrary topologies (node, zone, etc.) These affinity rules are now be specified in a new-in-1.6 `affinity` field of the PodSpec. Kubernetes 1.6 continues to support the alpha `scheduler.alpha.kubernetes.io/affinity`...