if (!llvm::hasSingleElement(func)) {
    LLVM_DEBUG(llvm::dbgs() << "Expect single block function, skip conversion "
                               "to tf_executor dialect\n");
    return;
  }
  auto loc = func.getLoc();
  mlir::Block& body = func.front();
  // Find region of interest and ReturnOp.
  auto copy_range = body.without_terminator();
  if (copy_range.begin() != copy_range.end() &&

  // In some cases the shared_name attribute doesn't have the same
  // tensor name in the model, so we first try to use the location
  // then fallback to shared_name attribute.
  if (auto loc = mlir::dyn_cast<NameLoc>(var_handle_op->getLoc()))
    return loc.getName().str();
  return var_handle_op.getSharedName().str();
}

std::vector<std::string> GetVariableNames(
    llvm::ArrayRef<TF::VarHandleOp> var_handle_ops) {
  std::vector<std::string> names;

    ++dim;
  }

  auto permutation_and_shape = GetPermutationAndTransposedShape(
      permutation_array, updates_type, rewriter);

  auto transposed_updates = rewriter.create<mhlo::TransposeOp>(
      scatter_op->getLoc(), permutation_and_shape.shape, updates,
      permutation_and_shape.permutation);

  updates = transposed_updates;
  updates_type = permutation_and_shape.shape;
  return success();
}

}  // end namespace odml

};

template <typename OP>
int replace(OP op, int count) {
  OpBuilder builder(op);
  std::string name = absl::StrFormat("_iterator%d", count++);

  auto new_op = builder.create<TF::IteratorOp>(
      op->getLoc(), op->getResultTypes()[0], name, /*container=*/"",
      op.getOutputTypes(), op.getOutputShapes());
  op->getResults()[0].replaceAllUsesWith(new_op->getResults()[0]);
  if (op->use_empty()) op->erase();
  return count;
}

  context.loadDialect<TF::TensorFlowDialect, quant::QuantizationDialect,
                      func::FuncDialect>();
  EmptyPatternRewriter pattern_rewriter(builder);
  Value value = CreateConstValue<float>(builder, module->getLoc(), {1024, 2},
                                        SmallVector<float>(2048, 0));

  QuantizationComponentSpec quant_spec;
  quant_spec.set_quantization_component(
      QuantizationComponentSpec::COMPONENT_WEIGHT);

  ModuleOp module = getOperation();
  module.walk([&](tf_device::LaunchFuncOp launch) {
    OpBuilder builder(launch);
    auto call_op = builder.create<TF::PartitionedCallOp>(
        module.getLoc(), launch.getResultTypes(), launch.getOperands(),
        SymbolRefAttr::get(builder.getContext(), launch.getFunc()),
        /*config=*/builder.getStringAttr(""),
        /*config_proto=*/builder.getStringAttr(""),

                                                .cast<quant::QuantizedType>()
                                                .getStorageType());
        auto new_op = builder.create<TFR::CastOp>(
            returned_op->getLoc(), new_type, returned_op.getArg());
        returned_value.set(new_op.getResult());
        if (returned_op.use_empty()) {
          returned_op.erase();
        }
      } else {

    void killDaemonOnly() {
        new ProcessFixture(context.pid).kill(false)
    }

    @Override
    String toString() {
        "Daemon with context $context"
    }

    @Override
    String getLog() {
        return daemonLog.text
    }

    @Override
    File getLogFile() {
        return daemonLog.file
    }

public interface DaemonFixture {
    /**
     * Returns the context information of this daemon.
     */
    DaemonContext getContext();

    /**
     * Returns the log for this daemon.
     */
    String getLog();

    /**
     * Returns the log file for this daemon.
     */
    File getLogFile();

    /**
     * Returns whether the log file contains a given String.
     *

  SmallVector<NamedAttribute> attrs{op->getAttrs()};
  attrs.push_back(call_target);
  auto custom_call = rewriter.create<mlir::stablehlo::CustomCallOp>(
      op->getLoc(), op->getResultTypes(), op->getOperands(), attrs);
  rewriter.replaceOp(op, custom_call.getResults());
  return success();
}

}  // namespace

template <typename OpTy>