const int BUFFER_SIZE = 10;
  char buffer[BUFFER_SIZE];
  std::strncpy(buffer, "VALID", BUFFER_SIZE);
  TFE_OpSetAttrString(op, "padding", buffer, std::strlen(buffer));
  // Overwriting value in "buffer", should be fine since TFE_Op
  // shouldn't be holding on to it.
  std::strncpy(buffer, "NHWC", BUFFER_SIZE);
  TFE_OpSetAttrString(op, "data_format", buffer, std::strlen(buffer));

  // functions, and their send/recvs corountines).
  config.set_inter_op_parallelism_threads(1);

  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(config, ret));
  return ret;
}

TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) {
  tensorflow::RunOptions options;
  if (enable_full_trace) {

  option_value.type_tag = TF_Filesystem_Option_Type_Buffer;
  option_value.num_values = values.size();
  std::vector<TF_Filesystem_Option_Value_Union> option_values(values.size());
  for (size_t i = 0; i < values.size(); i++) {
    memset(&option_values[i], 0, sizeof(option_values[i]));
    option_values[i].buffer_val.buf = const_cast<char*>(values[i].c_str());
    option_values[i].buffer_val.buf_length = values[i].size();
  }

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  auto* cell2 = TFE_MonitoringGetCellStringGauge2(gauge2, "foo", "bar");
  TFE_MonitoringStringGaugeCellSet(cell2, "str");
  auto* buf = new TF_Buffer;
  TFE_MonitoringStringGaugeCellValue(cell2, buf);
  string data(static_cast<const char*>(buf->data), buf->length);
  TF_DeleteBuffer(buf);
  EXPECT_EQ(data, "str");
  TFE_MonitoringDeleteStringGauge2(gauge2);

struct TF_SessionOptions {
  tensorflow::SessionOptions options;
};

struct TF_DeprecatedSession {
  tensorflow::Session* session;
};

struct TF_Library {
  void* lib_handle;
  TF_Buffer op_list;
};

struct TF_Graph {
  TF_Graph();

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

  if (c_api_buffer == nullptr) {
    return absl::InternalError(
        "The PjRtBuffer in the tensor is not type PjRtCApiBuffer.");
  }
  return c_api_buffer->c_buffer();
}

absl::Status SetPjRtCBufferToTensor(PJRT_Buffer* c_buffer,
                                    xla::PjRtCApiClient* c_api_client,
                                    Tensor* tensor) {
  auto buffer = std::make_unique<xla::PjRtCApiBuffer>(c_api_client, c_buffer);

    memory usage.
*   Fixed bfloat16 integration of LIBXSMM sparse mat-mul.
*   Improved performance and reduce memory usage by allowing ops to forward
    input buffers to output buffers and perform computations in-place.
*   Improved the performance of CPU assignment for strings.
*   Speed up matrix * vector multiplication and matrix * matrix with unknown
    shapes.

  }
  *result = TF_TensorFromTensor(**status_or, &status->status);
  return status->status.ok();
}

TF_ApiDefMap* TF_NewApiDefMap(TF_Buffer* op_list_buffer, TF_Status* status) {
  tensorflow::OpList op_list;
  if (!op_list.ParseFromArray(op_list_buffer->data, op_list_buffer->length)) {
    status->status = InvalidArgument("Unparseable OpList");
    return nullptr;
  }
  status->status = absl::OkStatus();

#include "tensorflow/c/eager/abstract_op_attrs.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/framework/attr_value.pb.h"

// An equivalent of a tensorflow::NameAttrList protocol buffer, but used in ways
// that sometimes do not require serialization.
typedef struct TFE_OpAttrs TFE_OpAttrs;

typedef struct TFE_Context TFE_Context;
typedef struct TFE_Op TFE_Op;

namespace tensorflow {

// Disables tracing of RunMetadata on the ops executed from this context.
TF_CAPI_EXPORT extern void TFE_ContextDisableRunMetadata(TFE_Context* ctx);

// Populates the passed-in buffer with a serialized RunMetadata protocol buffer
// containing any run metadata information accumulated so far and clears this
// information.
// If async mode is enabled, this call blocks till all currently pending ops are
// done.