// Create return op for device computation region in the paralle_execute op
  Operation* after_op_r = builder.create<mlir::tf_device::ReturnOp>(
      new_device_cluster.getLoc(), device_results);

  builder.setInsertionPointToEnd(&parallel_execute_device_block);

  // Create return op for the new device cluster op
  builder.create<mlir::tf_device::ReturnOp>(device_cluster.getLoc(),

    auto begin_op =
        rewriter.create<arith::ConstantOp>(op->getLoc(), type, begin_attr);
    auto end_attr = DenseElementsAttr::get<int32_t>(type, padded_end);
    auto end_op =
        rewriter.create<arith::ConstantOp>(op->getLoc(), type, end_attr);
    auto stride_attr = DenseElementsAttr::get<int32_t>(type, padded_stride);
    auto stride_op =
        rewriter.create<arith::ConstantOp>(op->getLoc(), type, stride_attr);

      tensorflow::GetTypeFromTFTensorShape({1}, shape_dtype);
  auto expanded_start_index = rewriter->create<TF::ExpandDimsOp>(
      loc, vector_type, start_index, scalar_zero);
  auto start_position = rewriter->create<TF::ConcatOp>(
      loc, position_type, scalar_zero,
      ArrayRef<Value>({expanded_start_index, partial_start_position}));

  // Create the slice size tensor. This is done by concatenating `size` and
  // `partial_size`.

  TF_Run(session, run_options, nullptr, nullptr, 0, nullptr, nullptr, 0,
         nullptr, 0, run_metadata, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ("Session was not created with a graph before Run()!",
            string(TF_Message(s)));
  TF_DeleteBuffer(run_metadata);
  TF_DeleteBuffer(run_options);

  TF_DeleteDeprecatedSession(session, s);

  TensorType zero_point_type = scale_type.clone(rewriter.getI32Type());
  scale = rewriter.create<TF::ConstOp>(
      loc, scale_type,
      DenseFPElementsAttr::get(scale_type,
                               {static_cast<float>(qtype.getScale())}));
  zero_point = rewriter.create<TF::ConstOp>(
      loc, zero_point_type,
      DenseIntElementsAttr::get(zero_point_type,

  // (a) read-only. These reads can be replaced by a hoisted read created
  //        before the WhileOp (similar to if and case).
  // (b) written: since the value is written in the loop (which can only in
  //        loop body, all these will become loop variables. Since all resource
  //        variables are removed from the loop variabled during
  //        canonicalizationW, we need to create new operand/result slots. The

    func::FuncOp parent_func, ModuleOp module, const std::string& name,
    bool flag_for_inlining) {
  // Moves all of the Operations in 'ops' into a newly created func.FuncOp
  // function named 'name' and replaces the original ops with a call to the
  // newly created function using a tf.StatefulPartitionedCall. Here,
  // 'parent_func' is the function that holds the original set of ops.

  if (!shaped_type) {
    return errors::Internal("Constant doesn't have a shape");
  }
  auto op = builder.create<tfl::ExternalConstOp>(
      loc, shaped_type, builder.getI32IntegerAttr(buffer_index));
  return op.getOperation();
}

// TODO(b/172664358): Creates a new op instead of reusing constant op.
// Creates a constant op with "tfl.is_variable" attribute to represent stateful