--hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \
    --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
    --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \
    --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \
    --hash=sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269 \

// will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(
    TFE_TensorHandle* h, TF_Status* status);

// Returns the name of the device in whose memory `h` resides.
//
// This function will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleBackingDeviceName(

libcudnn8=8.9.6.50-1+cuda12.2
libcudnn8-dev=8.9.6.50-1+cuda12.2

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

  std::unique_ptr<TFE_Context, decltype(&TFE_DeleteContext)> context(
      TFE_NewContext(opts.get(), status.get()), TFE_DeleteContext);
  tensorflow::ServerDef server_def = GetServerDef("worker", 3);

  // This server def has the task index set to 0.
  std::string serialized = server_def.SerializeAsString();

  server_def.set_task_index(1);
  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(

Violations of the Code of Conduct can occur in any setting, even those unrelated to the project. We will only consider complaints about conduct that has occurred within one year of the report.


## Enforcement

}

}  // end extern "C"

namespace tensorflow {

void RecordMutation(TF_Graph* graph, const TF_Operation& op,
                    const char* mutation_type) {
  // If any session has already run this node_id, mark this session as
  // unrunnable.
  for (auto it : graph->sessions) {
    mutex_lock session_lock(it.first->mu);
    if (it.first->last_num_graph_nodes > op.node.id()) {

# Use lld as the linker
build --linkopt="-fuse-ld=lld"
build --linkopt="-lm"
build --linkopt="-Wl,--undefined-version"

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.

                                       bool remote_func_outputs = false,
                                       bool has_packed_input = false) {
  return TestRemoteExecuteSilentCopies(async, remote, /*func=*/true,
                                       heavy_load_on_streaming_rpc,
                                       remote_func_outputs, has_packed_input);
}

TEST(CAPI, RemoteExecuteSilentCopiesAsyncFunc) {

  return tensorflow::errors::Unimplemented(
      "SetAttrFunction has not been implemented yet.");
}
Status SetAttrFunctionName(AbstractOperation* op_, const char* attr_name,
                           const char* value, size_t length,
                           ForwardOperation* forward_op_) {
  return tensorflow::errors::Unimplemented(
      "SetAttrFunctionName has not been implemented "
      "yet.");
}

- We inspect every incoming PR and add certain labels to the PR such as `size:`,
  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has
  sufficient description, if applicable unit tests are added, if it is a
  reasonable contribution (meaning it is not a single liner cosmetic PR).

**2. Valid?**