return builder->getFloatAttr(builder->getF32Type(), value.f());
    case AttrValue::kB:
      return builder->getBoolAttr(value.b());
    case AttrValue::kType: {
      mlir::Type type;
      TF_RETURN_IF_ERROR(ConvertDataType(value.type(), *builder, &type));
      return mlir::TypeAttr::get(type);
    }
    case AttrValue::kShape:
      return ConvertTensorShapeProto(value.shape(), builder->getContext());
    case AttrValue::kTensor:

#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/errors.h"

namespace tensorflow {

using mlir::Builder;
using mlir::ShapedType;
using mlir::Type;

Status ConvertDataType(DataType dtype, Builder builder, Type* type) {
  switch (dtype) {
    case DT_HALF:
      *type = builder.getF16Type();
      return absl::OkStatus();
    case DT_FLOAT:
      *type = builder.getF32Type();

#include "tensorflow/core/framework/types.pb.h"

namespace tensorflow {

using tsl::StatusOr;

// Converts the TensorFlow DataType 'dtype' into an MLIR (scalar) type.
Status ConvertDataType(DataType dtype, mlir::Builder builder, mlir::Type* type);

// Converts a scalar MLIR type to a TensorFlow Datatype.
Status ConvertScalarTypeToDataType(mlir::Type type, DataType* dtype);

  ctx.loadAllAvailableDialects();
}

Status ConvertDataTypeToTensor(tensorflow::DataType dtype, Builder builder,
                               Type* type) {
  Status s = tensorflow::ConvertDataType(dtype, builder, type);
  if (s.ok()) *type = UnrankedTensorType::get(*type);
  return s;
}

class MlirTensor : public TracingTensorHandle {
 public:
  explicit MlirTensor(Value value)

  for (auto ty : input_dtys) {
    mlir::Type elt_ty;
    TF_RETURN_IF_ERROR(ConvertDataType(ty, builder, &elt_ty));
    mlir::TensorType mlir_ty = mlir::UnrankedTensorType::get(elt_ty);
    input_tys.push_back(mlir_ty);
  }
  for (auto ty : output_dtys) {
    mlir::Type elt_ty;
    TF_RETURN_IF_ERROR(ConvertDataType(ty, builder, &elt_ty));
    mlir::TensorType mlir_ty = mlir::UnrankedTensorType::get(elt_ty);

                                           Builder* builder) {
  const auto& input_dtype = input_tensor.dtype();
  const auto& input_shape = input_tensor.shape();
  Type elt_type;
  TF_RETURN_IF_ERROR(ConvertDataType(input_dtype, *builder, &elt_type));
  SmallVector<int64_t, 4> shape;
  ConvertToMlirShape(input_shape, &shape);
  auto type = RankedTensorType::get(shape, elt_type);

#define CONVERT_FLAT(DTYPE, CTYPE) \

                                   context, builder);
  }
  DataType dtype = node.properties()->input_types[idx];
  mlir::Type element_type;
  TF_RETURN_IF_ERROR(ConvertDataType(dtype, builder, &element_type));
  return mlir::UnrankedTensorType::get(element_type);
}

absl::StatusOr<mlir::Type> ImporterBase::InferOutputType(
    const Node& node, int idx, mlir::Builder builder) {

            "Argument ", idx,
            " is an uninitialized resource variable which is currently"
            " unsupported in the MLIR-based TPU bridge"));
      }
      TF_RETURN_IF_ERROR(ConvertDataType(xla_arg.type, builder, &element_type));
      TF_ASSIGN_OR_RETURN(TensorShape arg_shape,
                          GetTensorShapeFromXlaArgument(xla_arg));
      auto resource_shape = arg_shape.dim_sizes();