DataType data_type() const { return data_type_; }
  Status status() const { return status_; }
  const Tensor& tensor() const { return tensor_; }

 private:
  Status status_;
  Output output_ = Output(Operation(nullptr), 0);
  Tensor tensor_;
  const std::string node_name_ = "";
  int32 index_ = 0;
  DataType data_type_ = DT_INVALID;
};

    auto* ret = emptyTensor->tensor;
    delete emptyTensor;
    return ret;
  }

  Tensor tensor;
  if (!tensor.CopyFrom(src, src.shape())) {
    return nullptr;
  }
  return new tensorflow::TensorInterface(std::move(tensor));
}

// Non-static for testing.
TF_Tensor* TF_TensorFromTensor(const tensorflow::Tensor& src, Status* status) {

    %tl_set_item = "tf.TensorListSetItem"(%tl, %idx, %elem_1) : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<8xf32>) -> tensor<!tf_type.variant<tensor<*xf32>>>
    %elem_shape_2 = "tf.Const"() {value = dense<-1> : tensor<1xi32>} : () -> tensor<1xi32>

                const OutputList& tensors, std::vector<Tensor>* out);

/// Computes the output 'tensor', returning the resulting tensor in 'out'.
void GetTensor(const Scope& scope, Output tensor, Tensor* out);

// Computes the output 'tensor', returning the resulting tensor in 'out'.
// assign_vars are extra outputs that should be run
// e.g. to assign values to variables.

  let summary = "Loads a restored variable tensor as a tensor future";

  let description = [{
    This op loads a restored variable tensor as a tensor future. It is a
    replacement of `tf.ReadVariableOp`.

    This op returns a scalar string tensor containing the restored variable name, which 
    is composed from `container_name` and `shared_name` from a `var_handle` and can be

def PromiseOp: TensorflowMlrt_Op<"promise", []> {
  let summary = "Set a tensor in a promise";

  let description = [{
    Set a tensor in a promise.

    $promise: A value of type !mlrt.promise. The underlying value must be a tensorflow tensor.
    $tensor: A tensorflow tensor.
  }];

  let arguments = (ins
    MlrtPromiseType:$promise,
    TFTensorType:$tensor
  );

  let assemblyFormat = "operands attr-dict";
}

  Status RunCallable(CallableHandle handle,
                     const std::vector<Tensor>& feed_tensors,
                     std::vector<Tensor>* fetch_tensors,
                     RunMetadata* run_metadata);

  /// \brief Invokes the subgraph named by `handle` with the given options and
  /// input tensors.
  ///
  /// The order of tensors in `feed_tensors` must match the order of names in

  // Create a DeviceMemoryBase from a Tensor. The Tensor can be an XlaTensor, in
  // which case the returned value is shaped_buffer()->root_buffer(), or a
  // normal Tensor in which case the returned value is
  // {tensor.tensor_data().data(), tensor.tensor_data().size}.
  static se::DeviceMemoryBase DeviceMemoryFromTensor(const Tensor& tensor);

  // Assign the internal ShapedBuffer to new memory for the given dtype and

func.func @single_restore_group(%arg0: tensor<!tf_type.string>) -> (tensor<*xf32>, tensor<*xi32>) {
  %0 = "tf.Const"() {value = dense<"foo"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
  %1 = "tf.Const"() {value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
  %2 = "tf.RestoreV2"(%arg0, %0, %1) : (tensor<!tf_type.string>, tensor<1x!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<*xf32>

// as tensor names may have changed via MLIR constant folding process.
bool ExpectEqualTensor(const Tensor* tensor, const Tensor* expected_tensor) {
  return (tensor->is_variable() == expected_tensor->is_variable()) &&
         (GetAsVector(tensor->shape()) ==
          GetAsVector(expected_tensor->shape()));
}

// Finds the match of the quantized tensor from the possible tensors. Each