}
    }

    // Move inner ops from island to block containing graph.
    auto &island_body = island_op.GetBody().getOperations();
    Operation *operation = op.getOperation();
    operation->getBlock()->getOperations().splice(
        operation->getIterator(), island_body, island_body.begin(),
        std::prev(island_body.end()));
    rewriter.replaceOp(op, new_rets);

    auto call = llvm::cast<CallOpInterface>(pcall_op.getOperation());
    auto callee =
        llvm::cast<FuncOp>(call.resolveCallable(&symbol_table_collection));
    callee->setAttr(noinline_attr_name, builder.getBoolAttr(true));
  }
  return mlir::success();
}

void XlaClusterFormationPass::runOnOperation() {
  ModuleOp module = getOperation();
  SymbolTableCollection symbol_table_collection;

  if (llvm::isa<WhileOp, WhileRegionOp>(op))
    op->removeAttr(kShapeInvariantAttr);
}
void DropWhileShapeInvariantPass::runOnOperation() {
  getOperation().walk([](Operation* op) { DropWhileShapeInvariantAttr(op); });
}

void DropWhileShapeInvariantInDeviceClusterPass::runOnOperation() {
  getOperation().walk([](tf_device::ClusterOp cluster) {
    cluster.walk([](Operation* op) { DropWhileShapeInvariantAttr(op); });
  });
}
}  // namespace

    Operation* inlined_point = call_op.getOperation();
    Operation* after_inlined_point =
        &*std::next(Block::iterator(call_op.getOperation()));

    // Use the inliner to replace all the uses of the call_op by its
    // composition.
    if (failed(inlineCall(inliner,
                          cast<CallOpInterface>(call_op.getOperation()),

  int64_t total_size_transferred = 0;
  for (auto input : to_graph.getOperands()) {
    Operation* input_op = input.getDefiningOp();
    if (input_op && input_op == from_graph.getOperation()) {
      auto input_type =
          mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
      if (input_type == nullptr || !input_type.hasStaticShape()) continue;

 public:
  using OpRewritePattern<SrcOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(SrcOp srcop,
                                PatternRewriter &rewriter) const final {
    Operation *op = srcop.getOperation();
    bool allTypesFp = true;
    bool allTypesQuantizedOrInt = true;
    for (auto operand : op->getOperands()) {
      ShapedType type = mlir::dyn_cast<ShapedType>(operand.getType());
      if (!type) continue;

    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();

    RewritePatternSet patterns(&getContext());
    TF::PopulateDecomposeResourceOpsPatterns(&getContext(), &patterns);

    if (failed(applyPatternsAndFoldGreedily(getOperation(),
                                            std::move(patterns)))) {
      getOperation().emitError() << kBadDecompositionMessage;
      signalPassFailure();
    }
  }
};

#define GEN_PASS_DEF_DECOMPOSERESOURCEOPSINCLUSTERPASS

template <typename OpTy, typename... Args>
OpTy CreateTfOp(RewriterBase &b, Operation *op, Args &&...args) {
  auto ret = b.create<OpTy>(op->getLoc(), std::forward<Args>(args)...);
  CopyDeviceAndUnderscoredAttributes(op, ret.getOperation());
  return ret;
}

// Helper function to replace TF op with another op while copying all underscore
// attributes from the TF op.
// TODO(jpienaar): This is a workaround until behavior is established.

      builder.create<tf_device::ReturnOp>(op->getLoc(), op->getResults())
          .getOperation();
  MLIRContext* context = launch_op.getContext();
  op->removeAttr(StringAttr::get(context, kDeviceAttr));
  op->moveBefore(return_op);
}

void DeviceAttributeToLaunch::runOnOperation() {
  const Dialect* tf_dialect = getContext().getLoadedDialect("tf");

  getOperation().walk([&](Operation* op) {