}

def TfSavedModel_GlobalTensorOp : TfSavedModel_Op<"global_tensor"> {
  let summary = "Represents a global tensor value.";
  let description = [{
    Represents a tensor that is not bound to the lifetime of any particular
    function. Such tensors can be marked as mutable via the `is_mutable`
    attribute.

    These tensors are bound to the arguments of func ops via the
    `tf_saved_model.bound_input` argument attr.

  // CHECK: func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %val = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
    // CHECK: "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
    func.return
  }

    std::vector<int> tensors;  // The tensors that are used (input or output) by
                               // this operation. They needn't correspond to
                               // tensors in the TF graph -- we may add fake
                               // tensors to model memory consumed in addition
                               // to input and output tensors. This vector is

// model.
table SubGraph {
  // A list of all tensors used in this subgraph.
  tensors:[Tensor];

  // Indices of the tensors that are inputs into this subgraph. Note this is
  // the list of non-static tensors that feed into the subgraph for inference.
  inputs:[int];

  // Indices of the tensors that are outputs out of this subgraph. Note this is
  // the list of output tensors that are considered the product of the

                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.
  void DeleteTrace(const AbstractTensorHandle*);

// model.
table SubGraph {
  // A list of all tensors used in this subgraph.
  tensors:[Tensor];

  // Indices of the tensors that are inputs into this subgraph. Note this is
  // the list of non-static tensors that feed into the subgraph for inference.
  inputs:[int];

  // Indices of the tensors that are outputs out of this subgraph. Note this is
  // the list of output tensors that are considered the product of the

      if (value.type() != TaggedValue::Type::TENSOR) {
        return false;
      }
      auto spec = signature.tensor_spec();
      const auto& tensor = value.tensor();
      if (tensor->DataType() != spec.dtype) {
        return false;
      }
      tensorflow::PartialTensorShape tensor_shape;
      DCHECK(tensor->Shape(&tensor_shape).ok());
      if (!tensor_shape.IsCompatibleWith(spec.shape)) {

    }

    tensors.emplace_back(
        device_->GetAllocator(tensorflow::AllocatorAttributes()), expr.dtype(),
        shape_or.value());

    tensorflow::Tensor& tensor = tensors.back();
    tensorflow::XlaExpression::AssignExpressionToTensor(expr, &tensor);
    inputs.emplace_back(&tensor);
  }

  return success();
}

    for (const TensorHandlePtr& tensor : tensors_) {
      result.emplace_back(tensor.get());
    }
    return result;
  }

 private:
  ParallelTensor(const ParallelDevice& device,
                 std::vector<TensorHandlePtr> tensors,
                 absl::Span<const int64_t> shape, const TF_DataType dtype)
      : device_(device),
        tensors_(std::move(tensors)),

// CHECK-NEXT:    buffer: 2
// CHECK-NEXT:  } ],
// CHECK-NEXT:  signature_defs: [  ]
// CHECK-NEXT: }
func.func @main(%arg0 : tensor<!tf_type.variant<tensor<2xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> {
  func.return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>