EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  Define(1, {add}, {feed1, feed2}, {add}, {}, true);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
  EXPECT_EQ(string("The source of control edge [id=3 scalar:-1 -> add:-1] "
                   "is not in the body. Encountered while creating "
                   "function 'MyFunc'"),
            string(TF_Message(s_)));
}

        for more details.
    *   Added `dataset_id` to `tf.data.experimental.service.register_dataset`.
        If provided, `tf.data` service will use the provided ID for the dataset.
        If the dataset ID already exists, no new dataset will be registered.
        This is useful if multiple training jobs need to use the same dataset

   pid_t ut_pid;			/* Process ID of login process.  */
-  char ut_line[UT_LINESIZE];	/* Devicename.  */
-  char ut_id[4];		/* Inittab ID.  */
-  char ut_user[UT_NAMESIZE];	/* Username.  */
-  char ut_host[UT_HOSTSIZE];	/* Hostname for remote login.  */
+  char ut_line[UT_LINESIZE]
+    __attribute_nonstring__;	/* Devicename.  */
+  char ut_id[4]
+    __attribute_nonstring__;		/* Inittab ID.  */

           {{"T", DT_FLOAT},
            {"group_size", static_cast<int>(devices.size())},
            {"group_key", 0},
            {"instance_key", 0},
            {"merge_op", "Add"},
            {"final_op", "Id"},
            {"subdiv_offsets", std::vector<int>()}},
           /*dep=*/{"assert"}},
      });
  TF_ASSERT_OK(ContextFromInterface(unwrap(context.get()))
                   ->AddFunctionDef(assert_and_collective));

  - type: input
    id: Python
    attributes:
      label: Python version
      placeholder: e.g., 3.9
  - type: input
    id: Bazel
    attributes:
      label: Bazel version
      description: If compiling from source
  - type: input
    id: Compiler
    attributes:
      label: GCC/compiler version
      description: If compiling from source
  - type: input
    id: Cuda
    attributes:

                  .ok());
  ASSERT_TRUE(worker_server->Start().ok());

  // Update server def to replace the remote device with the device info on the
  // new worker (different incarnation ID).
  server_def.set_task_index(0);
  string serialized_update = server_def.SerializeAsString();
  TFE_ContextUpdateServerDef(ctx, 0, serialized_update.data(),
                             serialized_update.size(), status);

  if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);

  testsuite = ElemTree.SubElement(testsuites, 'testsuite')
  for url, invocation_results in result_store_dict.items():
    invocation_id = url.rsplit('/')[-1]
    if verbose:
      print(f'Creating testcase for invocation {invocation_id}')
    status = invocation_results['status']
    command = invocation_results.get('command')
    command_type = invocation_results.get('command_type')

  // followed by `TFE_ContextAsyncWait` to clear pending operations. It will be
  // used to cancel all other operations if any fails.
  //
  // Set step_id to configure the step id used for rendezvous creation. step id
  // of value -1 is reserved for global rendezvous and should not be set here.
  void StartExecute(TFE_Context* context,
                    const std::vector<ParallelTensor*>& inputs,

          echo "DATE=$(date +'%Y-%m-%d')" >> "$GITHUB_OUTPUT"
          # Converts r2.9 to just 2.9
          echo "REF=$(echo $GITHUB_REF_NAME | sed 's/r//g')" >> "$GITHUB_OUTPUT"
        id: vars
      -
        name: Build and push
        id: docker_build
        uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5 # v3.2.0
        with:
          push: true