ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(0, num_dims);
  TF_GraphGetTensorShape(graph, three_out_0, returned_dims, num_dims, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Clean up
  TF_DeleteGraph(graph);
  TF_DeleteStatus(s);
}

TEST(CAPI, Graph) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Make a placeholder operation.

      /*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(

    if (!mutation_warning.empty()) {
      // TODO(b/74949947): turn this back into an error status
      LOG(WARNING) << mutation_warning;
      session->graph->sessions[session].clear();
    }

    const auto num_nodes = graph.num_node_ids();
    if (session->last_num_graph_nodes < num_nodes) {
      // TODO(nolivia): check this on a subset of the graph instead of all of
      // it.

                           const size_t* lengths, int num_values) override {
    if (strcmp(attr_name, tensorflow::kColocationAttrName) == 0) {
      op_->colocation_constraints.clear();
      for (int i = 0; i < num_values; ++i) {
        op_->colocation_constraints.emplace(static_cast<const char*>(values[i]),
                                            lengths[i]);
      }
    } else {

    *   Add tf.keras.layers.AbstractRNNCell as the preferred implementation of
        RNN cell for TF v2. User can use it to implement RNN cell with custom
        behavior.
    *   Adding `clear_losses` API to be able to clear losses at the end of
        forward pass in a custom training loop in eager.
    *   Add support for passing list of lists to the `metrics` param in Keras
        `compile`.

                 const char* raw_device_name, TF_Status* status) {
  if (op_to_reset) {
    tensorflow::ImmediateExecutionOperation* op =
        tensorflow::unwrap(op_to_reset);
    op->Clear();
    status->status = op->Reset(op_or_function_name, raw_device_name);
  } else {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "op_to_reset should not be nullptr");
  }
}

    // clear the op_tape. We can clear the tape because ownership of
    // backward functions that will be used for gradient computation
    // has been transferred to `result`.
    for (const auto& op_pair : *op_tape) {
      op_pair.second.backward_function_deleter(
          op_pair.second.backward_function);
    }
    op_tape->clear();
  }
  return result;
}

  args = parser.parse_args()

  _TF_WORKSPACE_ROOT = args.workspace
  _TF_BAZELRC = os.path.join(_TF_WORKSPACE_ROOT, _TF_BAZELRC_FILENAME)

  # Make a copy of os.environ to be clear when functions and getting and setting
  # environment variables.
  environ_cp = dict(os.environ)

  try:
    current_bazel_version = retrieve_bazel_version()
  except subprocess.CalledProcessError as e: