TEST_F(CApiExperimentalFunctionTest, EmptyGraphRemoveNonExistentFunction) {
  TF_GraphRemoveFunction(func_graph_, "wrong_name", s_);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
  EXPECT_EQ(string("Tried to remove non-existent function 'wrong_name'."),
            string(TF_Message(s_)));
}

TEST_F(CApiExperimentalFunctionTest, GraphRemoveNonExistentFunction) {
  TF_Function* funcs[1];
  DefineFunction(func_name_, &func_);

  const char remote_device_name[] =
      "/job:localhost/replica:0/task:1/device:CPU:0";
  CheckRemoteMatMulExecutesOK(ctx, remote_device_name, local_device_name);

  // Adding a non-existent remote worker to cluster def. This should cause the
  // UpdateServerDef call to fail.
  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->mutable_job(0);

            string(TF_Message(s_)));
}

TEST_F(CApiFunctionTest, Attribute) {
  DefineFunction(func_name_, &func_);

  // Get non existent attribute
  TF_Buffer* attr_buf = TF_NewBuffer();
  TF_FunctionGetAttrValueProto(func_, "foo_attr", attr_buf, s_);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));

  // Set up distributed execution environment on local and remote tasks.
  // When `reset_context` is true, initialize new cluster context state based
  // on cluster configurations provided in `server_def`; otherwise, update
  // existing context state with the provided `server_def`. Contexts created
  // on remote tasks will be considered stale and garbage collected after
  // `keep_alive_secs` of inactivity.

        const string& name = pair.first;
        if ((name == prefix) || absl::StartsWith(name, prefix_cmp)) {
          status->status = InvalidArgument(
              "prefix [", prefix,
              "] conflicts with existing node in the graph named [", name, "]");
          return;
        }
      }
      child_scope_name = prefix;
    }
    tensorflow::Scope scope =

// does not set the device name. If it's not `NULL`, then it attempts to parse
// and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster
// than separately calling it because if the existing op has the same
// `raw_device_name`, it skips parsing and just leave as it is.
TF_CAPI_EXPORT extern void TFE_OpReset(TFE_Op* op_to_reset,
                                       const char* op_or_function_name,

  if [[ "$TFCI_DOCKER_REBUILD_UPLOAD_ENABLE" == 1 ]]; then
    docker push "$TFCI_DOCKER_IMAGE"
  fi
fi

# Keep the existing "tf" container if it's already present.
# The container is not cleaned up automatically! Remove it with:
# docker rm tf
if ! docker container inspect tf >/dev/null 2>&1 ; then
  # Pass all existing TFCI_ variables into the Docker container
  env_file=$(mktemp)
  env | grep ^TFCI_ > "$env_file"

      /*retries=*/0, status, /*clear_existing_contexts=*/false);
}

// Set server def with timeout.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status,
    bool clear_existing_contexts) {
  TFE_ContextSetServerDefWithTimeoutAndRetries(

            documented as temporary and to be removed.
            *   Uses of `tensors()` can be replaced by calling the existing
                methods `tensors_size()` and `tensor(int)`.
            *   Uses of the non-const overload of `nodes_and_registration` can
                be replaced by calling the existing methods `nodes_size()` and
                `context()`, and then calling the `GetNodeAndRegistration`

                                     serialized_server_def.size(),
                                     init_timeout_in_ms, status,
                                     /*clear_existing_contexts=*/false);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_DeleteContextOptions(opts);
  TF_DeleteStatus(status);
  return ctx;
}

tensorflow::ServerDef ReplaceTaskInServerDef(