Status CreateCond(const Scope& scope, const CondGraphBuilderFn& cond,
                  const std::vector<Output>& inputs, Output* output) {
  // The control dependency is for constants in the cond graph, and other ops
  // that do not depend on the loop variables. This ensures that these ops are
  // in the while loop frame (since they will indirectly depend on an Enter node
  // defining the frame) and that they are executed once per loop iteration.
  //

      BlankLine();
      BlockOpen("void TestFunction()");
      {
        Statement("int i = 1");
        BlankLine();
        BlockOpen("while (i == 1)");
        {
          CommentLine("Do nothing, really....");
          CodeLine("#if 0");
          Statement("call()");
          CodeLine("#endif");
          BlockClose();
        }
        BlockClose("  // comment ending TestFunction");
      }

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// Generated by the tf_library build rule.  DO NOT EDIT!
//
// This file contains a test and benchmark for the function generated by
// tfcompile.  All tokens of the form `{{TFCOMPILE_*}}` must be rewritten to
// real values before this file can be compiled.
//

    rewriter.eraseOp(call_op);
    func_op.eraseResult(0);
    func_op.insertResult(0, new_call_op.getResult(0).getType(),
                         /*resultAttrs=*/nullptr);

    // Modify the quantized fused function to do dequantize+relu(6).
    rewriter.setInsertionPoint(req_op);
    Value new_result = rewriter.create<mlir::stablehlo::UniformDequantizeOp>(
        req_op.getLoc(), func_op.getResultTypes()[0], req_op.getOperand());

  // First, we need to convert from functional, to executor dialect.
  pm.addNestedPass<FuncOp>(
      mlir::CreateFunctionalToExecutorDialectConversionPass());

  // Do a single pass to split the graph's single island op into an island per
  // op as expected by the following passes.
  pm.addNestedPass<FuncOp>(mlir::TF::CreateSplitIntoIslandPerOpPass());

    const jit::DeviceInfoCache& device_info_cache,
    const jit::DeviceSet& devices, bool allow_mixing_unknown_and_cpu,
    bool failure_to_pick_is_error) {
#define FAILED_TO_PICK_DEVICE(failing_status) \
  do {                                        \
    if (failure_to_pick_is_error) {           \
      return failing_status;                  \
    } else {                                  \

  Type type = GetGlobalType(source);
  std::string name = GetVariableName(source);
  if (auto existing = symbol_table.lookup<ml_program::GlobalOp>(name)) {
    // This might be of a different type, but we'll do a Cast later.
    return existing;
  }

  Attribute initial_value = GetInitialValue(source);
  if (!initial_value) {
    initial_value = builder.getZeroAttr(type);
    if (!initial_value) {

  }
  return it->second.potentially_written;
}

// Analyze the specified region for resource mutating operations, namely
// TF::AssignVariableOp, if so, set the resource associated as "potentially
// written". Do this recursively across the chain of regions via call or
// control flow ops.
// TODO(ashwinm): Move to iterative traversal.
LogicalResult ResourceAnalyzer::AnalyzeRegion(Region& region) {
  // Avoid infinite recursion.

      Function tf_function;
      for (const std::string& fn_name :
           child_node.function().concrete_functions()) {
        // Setup input signature.
        //
        // For now, we have to do a lot of manual digging through these and
        // assume they are tensorspecs. Once TODO(b/190203981) is done, we
        // should be able to pass along the `StructuredValue`s to an API in a
        // much cleaner way.

                                  PatternRewriter& rewriter) {
      // Move the first block of `tf_then_or_else_region` into the first block
      // of `scf_then_or_else_region` and do not add any arguments to the block.
      moveBlock(&tf_then_or_else_region.front(),
                &scf_then_or_else_region.front(), TypeRange(), rewriter);