int64_t bytes_transferred;
    if (offset < buf.size()) {
      size_t bytes_to_copy = std::min<size_t>(buf.size() - offset, n);
      memcpy(buffer, buf.data() + offset, bytes_to_copy);
      bytes_transferred = bytes_to_copy;
    } else {
      bytes_transferred = 0;
    }
    TF_SetStatus(status, TF_OK, "");
    return bytes_transferred;
  };

    tensorflow::AttrValue attr;
    for (const string& v : list) {
      attr.mutable_list()->add_s(v);
    }
    string bytes;
    attr.SerializeToString(&bytes);
    TF_SetAttrValueProto(desc, tensorflow::kColocationAttrName, bytes.data(),
                         bytes.size(), s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  }

  void VerifyCollocation(TF_Operation* op,

}

TF_Tensor* Int32Tensor(int32_t v) {
  const int num_bytes = sizeof(int32_t);
  int32_t* values = new int32_t[1];
  values[0] = v;
  return TF_NewTensor(TF_INT32, nullptr, 0, values, num_bytes,
                      &Int32Deallocator, nullptr);
}

TF_Tensor* DoubleTensor(double v) {
  const int num_bytes = sizeof(double);
  double* values = new double[1];
  values[0] = v;

  void* data = dl_tensor->data;

  if (dl_tensor->byte_offset != 0) {
    status->status = tensorflow::errors::InvalidArgument(
        "Unsupported byte_offset (", dl_tensor->byte_offset,
        ") from DLPack, must be zero");
    return nullptr;
  }

  size_t total_bytes = dl_tensor->dtype.bits / 8;
  for (int i = 0; i < num_dims; i++) {
    total_bytes *= dims[i];
  }

  if (dl_tensor->strides != nullptr &&

    block_size = value * 1024 * 1024;
  }
  const char* max_bytes_env = std::getenv(kMaxCacheSize);
  if (max_bytes_env && absl::SimpleAtoi(max_bytes_env, &value)) {
    max_bytes = static_cast<size_t>(value * 1024 * 1024);
  }
  const char* max_staleness_env = std::getenv(kMaxStaleness);
  if (max_staleness_env && absl::SimpleAtoi(max_staleness_env, &value)) {
    max_staleness = value;
  }

// must point to an array of length at least `num_values`.
//
// The elements of values will point to addresses in `storage` which must be at
// least `storage_size` bytes in length.  `num_values` and `storage` can be
// obtained from TF_GetNodesToPreserveSize
//
// Fails if storage_size is too small to hold the requested number of strings.
TF_CAPI_EXPORT extern void TF_GetNodesToPreserveList(

  void (*cleanup)(TF_RandomAccessFile* file);

  /// Reads up to `n` bytes from `*file` starting at `offset`.
  ///
  /// The output is in `buffer`, core TensorFlow owns the buffer and guarantees
  /// that at least `n` bytes are available.
  ///
  /// Returns number of bytes read or -1 in case of error. Because of this
  /// constraint and the fact that `ssize_t` is not defined in `stdint.h`/C++

                                        TF_Status* status) {
  const int num_bytes = v.size() * sizeof(float);
  float* values = new float[v.size()];
  memcpy(values, v.data(), num_bytes);
  int64_t dims = v.size();
  std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)> tensor(
      TF_NewTensor(TF_FLOAT, &dims, 1 /* num_dims */, values, num_bytes,
                   &FloatDeallocator, nullptr),
      TF_DeleteTensor);

  TF_DeleteBuffer(attr_buf);

  // Set attr
  tensorflow::AttrValue attr;
  attr.set_s("test_attr_value");
  string bytes;
  attr.SerializeToString(&bytes);
  TF_FunctionSetAttrValueProto(func_, "test_attr_name", bytes.data(),
                               bytes.size(), s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  // Get attr
  AttrValue read_attr;

  // make sure there is no information loss.
  if (proto_len > std::numeric_limits<int>::max()) {
    status->status = InvalidArgument(
        "proto_len (", proto_len,
        " bytes) is too large to be parsed by the protocol buffer library");
    return;
  }
  TensorShapeProto shape;
  if (shape.ParseFromArray(proto, static_cast<int>(proto_len))) {
    desc->node_builder.Attr(attr_name, shape);