// First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);
  TF_Status* status = TF_NewStatus();
  TF_Operation* arg = TF_FinishOperation(arg_descr, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_OperationDescription* id_descr =

        llvm::dyn_cast<TF::ConstOp>(fold_results[0].getDefiningOp()).getValue(),
        &tensor));
    xla::Shape xla_shape;
    TF_RETURN_IF_ERROR(tensorflow::TensorShapeToXLAShape(
        tensor.dtype(), tensor.shape(), &xla_shape));
    xla::PjRtClient::HostBufferSemantics host_buffer_semantics =
        xla::PjRtClient::HostBufferSemantics::kImmutableUntilTransferCompletes;
    TF_ASSIGN_OR_RETURN(
        auto buffer,

}

Node* KnownShapeBase(DataType dtype, absl::Span<const int> shape,
                     const GraphDefBuilder::Options& opts) {
  if (opts.HaveError()) return nullptr;
  NodeBuilder node_builder(opts.GetNameForOp("Const"), "Const",
                           opts.op_registry());
  TensorProto value;
  value.set_dtype(dtype);
  for (int dim : shape) {

    return false;
  }

  if (n->type_string() != "AssignAddVariableOp" &&
      n->type_string() != "AssignSubVariableOp") {
    return false;
  }

  DataType dtype;
  if (!TryGetNodeAttr(n->def(), "dtype", &dtype) || !DataTypeIsInteger(dtype)) {
    return false;
  }

  Node* const_input = nullptr;
  for (const Edge* e : n->in_edges()) {
    if (!e->IsControlEdge() && e->src()->IsConstant()) {

#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

// Specification for expected input/output and its type.
// DataType value of DT_INVALID signifies that we don't want to
// check the data type.
typedef std::pair<string, DataType> IOSpec;

const char* kFeedStackToString = "File \"feed.cc\", line 10, in alpha";

  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, "ReadVariableOp", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpAddInput(op, var, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  int num_retvals = 1;
  TFE_Execute(op, out_value, &num_retvals, status);

ElementsAttr GetTensorValueAsElementsAttr(
    TF::VarHandleOp var_handle_op, const tensorflow::Tensor& resource_tensor,
    const tensorflow::DeviceMgr* mgr, OpBuilder builder) {
  if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {
    return GetTensorValueAsElementsAttr(resource_tensor, builder);
  }

  auto handle = resource_tensor.scalar<tensorflow::ResourceHandle>()();

}

// Return true if the passed quantized type is unsigned.
bool QuantizedTypeIsUnsigned(Type type) {
  return TypeSwitch<Type, bool>(type)
      .Case<mlir::TF::Qint8Type>([](Type) { return false; })
      .Case<mlir::TF::Qint16Type>([](Type) { return false; })
      .Case<mlir::TF::Qint32Type>([](Type) { return false; })
      .Case<mlir::TF::Quint8Type>([](Type) { return true; })

    return false;
  }

  if (node.type_string() == "Const") {
    const AttrValue* attr = node.attrs().Find("dtype");
    if (!op_filter_.allow_string_consts && attr != nullptr &&
        attr->type() == DT_STRING) {
      *uncompilable_reason =
          "Const op with type DT_STRING is not supported by XLA.";
      return false;
    }
  }

    if (node->type_string() == "Const" && must_be_true) {
      const TensorProto* proto = nullptr;
      TF_RETURN_IF_ERROR(GetNodeAttr(node->def(), "value", &proto));

      Tensor tensor(proto->dtype());
      TF_RET_CHECK(tensor.FromProto(*proto));

      *predicate = tensor.scalar<bool>()() ? MakeTrue() : MakeFalse();
      return absl::OkStatus();
    }