shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method

#else
#define TF_CAPI_EXPORT __attribute__((visibility("default")))
#endif  // TF_CAPI_WEAK
#endif  // _WIN32
#endif  // SWIG

// TF_Bool is the C API typedef for unsigned char, while TF_BOOL is
// the datatype for boolean tensors.
#ifndef TF_Bool
#define TF_Bool unsigned char
#endif  // TF_Bool

// Macro used to calculate struct size for maintaining ABI stability across
// different struct implementations.

                 AbstractTensorHandle* const tensor_names,
                 AbstractTensorHandle* const shape_and_slices,
                 absl::Span<AbstractTensorHandle*> tensors,
                 absl::Span<DataType> dtypes, const char* name = nullptr,
                 const char* raw_device_name = nullptr);

// Saves tensors in V2 checkpoint format.
Status SaveV2(AbstractContext* ctx, AbstractTensorHandle* const prefix,

namespace tensorflow {

void XlaHostRecvDeviceContext::CopyDeviceTensorToCPU(
    const Tensor* device_tensor, StringPiece tensor_name, Device* device,
    Tensor* cpu_tensor, StatusCallback done) {
  DataType dtype = EncodePrimitiveTypeAsDataType(shape_.element_type()).value();
  TensorShape tensor_shape;
  Status status = XLAShapeToTensorShape(shape_, &tensor_shape);
  if (!status.ok()) {
    done(status);
    return;
  }

namespace libtf {
namespace impl {
/// @brief The TensorSpec struct.
///
/// The TensorSpec describes the shape and dtype of a Tensor.

struct TensorSpec {
  tensorflow::PartialTensorShape shape;
  tensorflow::DataType dtype;

  bool operator==(const TensorSpec& o) const {
    return dtype == o.dtype && shape.IsIdenticalTo(o.shape);
  }

  /// Overload AbslHashValue to make TensorSpec hashable.
  template <typename H>

  // DT_FLOAT, DT_HALF, DT_QINT8, and DT_QUINT8. When DT_HALF is used, the
  // `weight_quantization` flag needs to set to true. When DT_QUINT8 is used,
  // the `weight_quantization` flag needs to set to false.
  tensorflow::DataType inference_type = tensorflow::DT_FLOAT;

  // The input and output data type during inference. This flag is only used
  // when `inference_type` is different from DT_FLOAT. This flag can only be set

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

namespace mlir {
namespace TFL {

double PowerOfTwoBound(double value) {
  return std::pow(2, std::ceil(std::log2(value)));
}

tensorflow::DataType GetQuantizedInferenceType(bool is_signed,
                                               int number_of_bits) {
  if (is_signed && number_of_bits == 8) {
    return tensorflow::DT_QINT8;

  TF_CHECK_OK(device->resource_manager()->Create(
      container, "var1", new tensorflow::Var(tensorflow::DataType::DT_FLOAT)));
  TF_CHECK_OK(device->resource_manager()->Create(
      container, "var2", new tensorflow::Var(tensorflow::DataType::DT_FLOAT)));
}

Status FakeSession::Create(const tensorflow::GraphDef& graph) {
  return tensorflow::errors::Unimplemented("not available");
}

#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_def_builder.h"
#include "tensorflow/core/framework/shape_inference.h"

using ::tensorflow::DataType;
using ::tensorflow::OpDef;
using ::tensorflow::OpDefBuilder;
using ::tensorflow::OpDeprecation;
using ::tensorflow::OpShapeInferenceFn;
using ::tensorflow::Set_TF_Status_from_Status;
using ::tensorflow::Status;

  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }
  return absl::StrCat("TensorHandle(", value_string, ", shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()), ", device=\"",
                      device_name, "\")");
}

Status ImmediateExecutionTensorHandle::SummarizeValue(
    std::string& summary) const {
  Status status;
  AbstractTensorPtr resolved(