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.

    ::tensorflow::protobuf::TextFormat::PrintToString(graph_def, &serialized);
    LOG(INFO) << serialized;
  }
}

struct InductionVarInfo {
  Output induction_var;
  Output loop_cond;
};

// Creates an induction variable with the following structure (simplified for
// brevity):
//
//            +---------------+
//            | initial_value |
//            +---------------+
//              |
//              |

  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

  auto output_shape_kept_dims =
      ReducedShapeHelper(scope, input_shape, op.input(1));

  // This step "flips" any 1s with values from the input_shape, and
  // replaces remaining entries with 1. This creates a shape that
  // shows how much each dimension in the incoming gradient should be
  // replicated.
  // tile_scaling = [1, 3]
  auto tile_scaling = SafeDivHelper(scope, input_shape, output_shape_kept_dims);

    // if a special Identity node in the following pattern is clustered in.
    // That is, an Identity node in the loop cond computation is used to drive
    // const nodes consumed by the loop body.  If this Identity node goes into
    // the same cluster with nodes from the loop body, extra dependency is
    // created between the loop cond and body computations and it hinders the

      return;
    }
    for (Predicate* op : current->GetOperands()) {
      if (visited.insert(op).second) {
        stack.push_back(op);
      }
    }
  }
}

// Creates and owns Predicate instances.  Simplifies predicates as it creates
// them.
class PredicateFactory {
 public:
  Predicate* MakeAndPredicate(absl::Span<Predicate* const> operands) {
    return MakeAndOrImpl(operands, /*is_and=*/true);
  }

  EXPECT_EQ(clusters["B_dev1"], clusters["MatMul1_dev1"]);
}

// TODO(b/117085735): This form of clustering should be prevented.
TEST(XlaCompilationTest, NOT_DontClusterSpreadingNodes) {
  // MatMulSource below creates data for nodes on GPU0 and GPU1 and is placed
  // on GPU0. However, it should not be clustered with the next node on
  // GPU0, because that will prevent the node on GPU1 from beginning its work as