send_recv_nodes.push_back(n);
    }
  }
  EXPECT_EQ(num_send_from_host, 1);
  EXPECT_EQ(num_recv_at_host, 1);
  for (Node *n : send_recv_nodes) {
    Node *input_node;
    TF_CHECK_OK(n->input_node(n->num_inputs() - 1, &input_node));
    EXPECT_EQ(input_node, key_placeholder);

    bool has_control_edge_to_sequencer = false;
    for (const Edge *e : n->out_edges()) {
      if (e->IsControlEdge() && e->dst() == sequencer) {

    : OpKernel(ctx), platform_info_(XlaPlatformInfoFromDevice(ctx->device())) {}

void XlaRunOp::Compute(OpKernelContext* ctx) {
  VLOG(3) << "XlaRunOp " << def().name();
  Tensor key_tensor = ctx->input(ctx->num_inputs() - 1);

  bool use_pjrt =
      GetXlaOpsCommonFlags()
          ->tf_xla_use_device_api.IsEnabledInXlaCompileAndRunForDevice(
              platform_info_.device_type());

  if (use_pjrt) {

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddAttrs(ret, TFE_OpGetAttrs(other));
  int num_inputs = TFE_OpGetFlatInputCount(other, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  for (int input_index = 0; input_index < num_inputs; ++input_index) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(other, input_index, status);

// For inputs that take a list of tensors.
// inputs must point to TF_Output[num_inputs].
TF_CAPI_EXPORT extern void TF_AddInputList(TF_OperationDescription* desc,
                                           const TF_Output* inputs,
                                           int num_inputs);

// Call once per control input to `desc`.

    mlir::FunctionType callee_type = callee.getFunctionType();
    SmallVector<Type, 4> expected_callee_input_types;
    auto num_inputs = op.getInputs().size() / 2;
    for (unsigned i = 0; i < num_inputs; ++i) {
      auto input_type =
          mlir::dyn_cast<RankedTensorType>(op.getOperand(i).getType());
      auto scalar = RankedTensorType::get({}, input_type.getElementType());

                                        ? mhlo::Precision::DEFAULT
                                        : mlir::mhlo::Precision::HIGHEST;
  llvm::SmallVector<mlir::Attribute, 2> attr_vec;
  const int num_inputs = 2;
  for (int i = 0; i < num_inputs; i++) {
    attr_vec.push_back(
        mlir::mhlo::PrecisionAttr::get(builder->getContext(), precision));
  }
  return builder->getArrayAttr(attr_vec);
}

const std::vector<AttrView>& OpView::Attributes() const {
  return argument_attrs_;
}

const std::vector<OpArgumentView>& OpView::AllArguments() const {
  return all_arguments_;
}

int OpView::NumInputs() const { return input_args_.size(); }

int OpView::NumOutputs() const { return output_args_.size(); }

ArgView OpView::OnlyInput() const {
  CHECK_EQ(input_args_.size(), 1);  // Crash OK
  return input_args_.front();

      }
    }

    return mlir::UnrankedTensorType::get(element_type);
  };

  // Below we only try and do some shape inference for "source" ops which have
  // no inputs.
  if (node.num_inputs() > 0) return default_type();

  // Do some simply inference here to get the function arguments correct for
  // this common case.
  // TODO(jpienaar): Reconsider post refactoring shape functions.

	// do not block indefinitely (e.g. apply a timeout).
	Close() error

	// NumInput returns the number of placeholder parameters.
	//
	// If NumInput returns >= 0, the sql package will sanity check
	// argument counts from callers and return errors to the caller
	// before the statement's Exec or Query methods are called.
	//
	// NumInput may also return -1, if the driver doesn't know

      IsSingleCallRegion(while_region.getBody()), while_arg_matcher);

  // All existing inputs to while region are inputs to the functional while.
  auto new_inputs = llvm::to_vector<4>(while_region.getOperands());

  // All existing results will also be generated by the functional while.
  auto new_result_types = llvm::to_vector<4>(while_region.getResultTypes());