task_index: 1
protocol: "grpc"
)");

  TF_Status* status = TF_NewStatus();
  TF_Buffer* result = TFE_GetServerDef(expected_text_proto.c_str(), status);
  EXPECT_EQ(TF_GetCode(status), TF_OK);

  ServerDef actual;
  ASSERT_TRUE(actual.ParseFromArray(result->data, result->length));
  string actual_text_proto;
  tensorflow::protobuf::TextFormat::PrintToString(actual, &actual_text_proto);
  EXPECT_EQ(expected_text_proto, actual_text_proto);

  }

  status_ = registry_.Register("MatMul", MatMulRegisterer);
  ASSERT_EQ(errors::OK, status_.code()) << status_.message();

  bool transpose_a_vals[] = {false, false, true, true};
  bool transpose_b_vals[] = {false, true, false, true};
  float dA_vals[4][9] = {{24, 15, 6, 24, 15, 6, 24, 15, 6},
                         {18, 15, 12, 18, 15, 12, 18, 15, 12},
                         {24, 24, 24, 15, 15, 15, 6, 6, 6},

// exporting a full trace so other code can do backpropagation. Not thread-safe.
template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class GradientTape {
 public:
  // If `persistent` is true, GradientTape will not eagerly delete backward
  // functions (and hence the tensors they keep alive). Instead, everything
  // is deleted in ~GradientTape. Persistent GradientTapes are useful when

static bool register_tracing = [] {
  RegisterTracingEngineFactory("graphdef", GraphTracingFactory);
  SetDefaultTracingEngine("graphdef").IgnoreError();
  return true;
}();

}  // namespace graph
}  // namespace tracing

  # Select lines unique to expected_licenses, i.e. missing licenses
  comm -2 -3 $BATS_TEST_TMPDIR/expected_licenses $BATS_TEST_TMPDIR/actual_licenses | grep -v -f $BATS_TEST_TMPDIR/allowed_to_be_missing > $BATS_TEST_TMPDIR/actual_missing_licenses || true

  if [[ -s $BATS_TEST_TMPDIR/actual_extra_licenses ]]; then

        "@local_xla//xla/tsl/distributed_runtime/coordination:coordination_service_agent",
    ],
    alwayslink = 1,
)

cc_library(
    name = "c_api_experimental_reader",
    testonly = True,
    srcs = [
        "c_api_experimental_reader.cc",
    ],
    hdrs = [
        "c_api_experimental_reader.h",
        "tfe_monitoring_reader_internal.h",
    ],

    if (dev_type == device_type) {
      *device_name = dev_name;
      LOG(INFO) << "Found " << device_type << " device " << *device_name;
      TF_DeleteDeviceList(devices);
      return true;
    }
  }
  TF_DeleteDeviceList(devices);
  return false;
}

tensorflow::ServerDef GetServerDef(const string& job_name, int num_tasks) {
  tensorflow::ServerDef server_def;

    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "op_to_reset should not be nullptr");
  }
}

void TFE_ContextEnableGraphCollection(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->SetShouldStoreGraphs(true);
}

void TFE_ContextDisableGraphCollection(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->SetShouldStoreGraphs(false);
}

uint64_t TFE_GetContextId(TFE_Context* ctx) {
  tensorflow::EagerContext* context =

  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(

dill==0.3.7 \
    --hash=sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e \
    --hash=sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03
    # via -r requirements.in
dm-tree==0.1.8 \
    --hash=sha256:054b461f8176f4bce7a21f7b1870f873a1ced3bdbe1282c816c550bb43c71fa6 \
    --hash=sha256:0d3172394079a86c3a759179c65f64c48d1a42b89495fcf38976d11cc3bb952c \