/// 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.
void GetTensor(const Scope& scope, const std::vector<Output>& assign_vars,
               Output tensor, Tensor* out);

  if (reader_ != nullptr) return reader_->DebugString();
  return v2_reader_->DebugString();
}

void CheckpointReader::GetTensor(
    const string& name, std::unique_ptr<tensorflow::Tensor>* out_tensor,
    TF_Status* out_status) const {
  Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;

                FEMALE, MALE, OTHER
            }

            @Managed
            interface Person {
              String getName()
              void setName(String string)

              Gender getGender()
              void setGender(Gender gender)
            }

            class Rules extends RuleSource {
              @Model
              void p1(Person p1) {}
            }

            apply type: Rules

  const TensorSliceReader::VarToDataTypeMap& GetVariableToDataTypeMap() const;

  // Attempts to look up the tensor named "name" and stores the found result in
  // "out_tensor".
  void GetTensor(const string& name,
                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"

    llvm::SmallDenseMap<Value, SizeInfo>* buffer_to_size) {
  OpBuilder builder(list);
  Value buffer = builder.create<TF::IdentityOp>(
      list.getLoc(), ArrayRef<Type>{list.getTensor().getType()},
      ArrayRef<Value>{list.getTensor()});
  auto type = mlir::cast<TensorType>(buffer.getType());
  if (!type.hasStaticShape()) {
    return list.emitOpError("TensorListFromTensorOp input has unknown shape.");
  }

            putValue(data, "Received-Date", getReceivedDate(message));
            putValue(data, "Reply-To", message.getReplyTo());
            putValue(data, "Sender", message.getSender());
            putValue(data, "Sent-Date", message.getSentDate());
            putValue(data, "Size", message.getSize());
            putValue(data, "Subject", message.getSubject());

    TF_EXPECT_OK(AddSymbolicGradients(scope_, {out0, out1, out2}, {z},
                                      {g0, g1, g2}, &grad_outputs));

    if (expected_grad.NumElements() > 0) {
      Tensor output;
      test::GetTensor(scope_, grad_outputs[0], &output);
      test::ExpectTensorEqual<int>(output, expected_grad);
    } else {
      EXPECT_EQ(NoGradient(), grad_outputs[0]);
    }
  }

  Scope scope_;
};

  };
  TensorType expected_ty;
  if (failed(GetReshapeOutputType(op.getTensor(), op.getShape(), error_handler,
                                  expected_ty)))
    return failure();

  auto output_ty = op.getType().dyn_cast<RankedTensorType>();
  if (!output_ty) return success();
  auto tensor_ty = op.getTensor().getType().cast<TensorType>();
  if (output_ty.hasStaticShape() && tensor_ty.hasStaticShape()) {

}

TF_Tensor* TF_CheckpointReaderGetTensor(TF_CheckpointReader* reader,
                                        const char* name, TF_Status* status) {
  std::unique_ptr<tensorflow::Tensor> tensor;
  reader->GetTensor(name, &tensor, status);
  if (!status->status.ok()) return nullptr;
  return tensorflow::TF_TensorFromTensor(*tensor, &status->status);
}

void TF_CheckpointReaderGetVariableShape(TF_CheckpointReader* reader,

    for (auto& use : tensorlist.getUses()) {
      if (auto push = llvm::dyn_cast<TensorListPushBackOp>(use.getOwner())) {
        auto element_type =
            mlir::dyn_cast<RankedTensorType>(push.getTensor().getType());
        if (!verify_and_update_potential_element_type(element_type))
          return false;
        add_to_worklist(push.getOutputHandle());
        continue;
      }