TF_OperationGetAttrTensorShapeProto(oper, "v", value, s_);
  EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  EXPECT_EQ(bytes.length(), value->length);
  EXPECT_EQ(0, memcmp(bytes.data(), value->data, value->length));
  TF_DeleteBuffer(value);
}

TEST_F(CApiAttributesTest, TensorShapeProtoList) {
  string bytes1, bytes2;
  tensorflow::TensorShapeProto proto;

    return 1;
  }

  char file_name[100];
  time_t t = time(NULL);
  snprintf(file_name, sizeof(file_name), "test-%d-%ld.txt", getpid(), t);

  size_t length = 2 + strlen(path) + strlen(file_name);
  char* full_path = malloc(length);
  snprintf(full_path, length, "%s/%s", path, file_name);

  TF_WritableFileHandle* h;
  TF_Status* status = TF_NewStatus();
  TF_NewWritableFile(full_path, &h, status);

}

void TF_SetAttrString(TF_OperationDescription* desc, const char* attr_name,
                      const void* value, size_t length) {
  tensorflow::StringPiece s(static_cast<const char*>(value), length);
  desc->node_builder.Attr(attr_name, s);
}

void TF_SetAttrStringList(TF_OperationDescription* desc, const char* attr_name,

  } else {
    shape_string = "<error computing shape>";
  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }
  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }
  Status s;

}

Status SetAttrString(AbstractOperation* op_, const char* attr_name,
                     const char* data, size_t length,
                     ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, StringPiece(data, length));
  return op_->SetAttrString(attr_name, data, length);
}
Status SetAttrInt(AbstractOperation* op_, const char* attr_name, int64_t value,
                  ForwardOperation* forward_op_) {

  // retrieving shapes of tensors they wrap until the custom device tensor's
  // shape is explicitly requested where possible.
  int (*num_dims)(void* data, TF_Status* status);

  // Computes the axis length at `dim_index`.
  int64_t (*dim)(void* data, int dim_index, TF_Status* status);

  void (*deallocator)(void* data);

  // Summarizes the value of this tensor. The caller takes ownership of the

  tensorflow::string value = cell->cell.value();
  void* data = tensorflow::port::Malloc(value.length());
  value.copy(static_cast<char*>(data), value.length(), 0);
  buf->data = data;
  buf->length = value.length();
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);
  };
}

TFE_MonitoringStringGauge0* TFE_MonitoringNewStringGauge0(

    }
    return absl::OkStatus();
  }

  Status SetAttrString(const char* attr_name, const char* data,
                       size_t length) override {
    tensorflow::StringPiece s(data, length);
    op_->node_builder.Attr(attr_name, s);
    return absl::OkStatus();
  }
  Status SetAttrInt(const char* attr_name, int64_t value) override {

  }
  TFE_DeleteOp(op);
  return ret;
}

void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, const void* value,
                         size_t length) {
  auto s = tensorflow::unwrap(op)->SetAttrString(
      attr_name, static_cast<const char*>(value), length);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

        scope string that will be prepended to op names.
*   `tf.data`:
    *   Promoting `tf.data.experimental.bucket_by_sequence_length` API to
        `tf.data.Dataset.bucket_by_sequence_length` and deprecating the
        experimental endpoint.
    *   Promoting `tf.data.experimental.get_single_element` API to