OpBuilder &rewriter) {
  auto i64_type = rewriter.getIntegerType(64);
  if (types.size() > 1) {
    llvm::SmallVector<mlir::Attribute> v;
    v.reserve(types.size());
    for (const mlir::Type &t : types) {
      if (mlir::isa<mhlo::TokenType>(t)) continue;
      auto layout = GetTPUInfeedLayout({t}, rewriter);
      if (failed(layout)) return failure();

	defaultPlugins.Score = mergePluginSet(logger, defaultPlugins.Score, customPlugins.Score)
	defaultPlugins.Reserve = mergePluginSet(logger, defaultPlugins.Reserve, customPlugins.Reserve)
	defaultPlugins.Permit = mergePluginSet(logger, defaultPlugins.Permit, customPlugins.Permit)
	defaultPlugins.PreBind = mergePluginSet(logger, defaultPlugins.PreBind, customPlugins.PreBind)

    }
    return FoldAdaptor(operation);
  }

  // Gets a list of ElementsAttr behind each constant operand.
  llvm::SmallVector<ElementsAttr> OperandData() {
    llvm::SmallVector<ElementsAttr> res;
    res.reserve(operation_->getNumOperands());
    for (auto opr : operation_->getOperands()) {
      auto op = llvm::dyn_cast<stablehlo::ConstantOp>(opr.getDefiningOp());
      res.push_back(op.getValue());
    }
    return res;
  }

  quant::QuantizedType convertFakeQuantAttrsToType(ConstFakeQuantPerAxis fqOp,
                                                   Type expressedType) const {
    SmallVector<double, 4> min, max;
    min.reserve(fqOp.getMin().size());
    max.reserve(fqOp.getMax().size());
    for (auto m : fqOp.getMin())
      min.push_back(cast<FloatAttr>(m).getValueAsDouble());
    for (auto m : fqOp.getMax())

                        llvm::SmallVectorImpl<int64_t>* shape) {
  shape->reserve(input_shape.dims());
  for (const auto& d : input_shape) {
    shape->push_back(d.size == kTFDynamicSize ? ShapedType::kDynamic : d.size);
  }
}

Status ConvertToMlirShape(const TensorShapeProto& input_shape,
                          llvm::SmallVectorImpl<int64_t>* shape) {
  shape->reserve(input_shape.dim_size());
  auto& dims = input_shape.dim();

  if (!session) return module.emitOpError() << "no session provided";

  // Read all resource variables from the session.
  std::vector<std::string> variable_names;
  variable_names.reserve(resource_names.size());
  for (StringRef name : resource_names) variable_names.push_back(name.str());

  std::vector<Tensor> resource_tensors;
  Status status = session->Run(
      /*inputs=*/{}, variable_names,

}

// varintAt writes varint v at byte position pos in buf. This always
// consumes traceBytesPerNumber bytes. This is intended for when the caller
// needs to reserve space for a varint but can't populate it until later.
// Use varintReserve to reserve this space.
func (buf *traceBuf) varintAt(pos int, v uint64) {
	for i := 0; i < traceBytesPerNumber; i++ {
		if i < traceBytesPerNumber-1 {
			buf.arr[pos] = 0x80 | byte(v)

  return var_handle_op.getSharedName().str();
}

std::vector<std::string> GetVariableNames(
    llvm::ArrayRef<TF::VarHandleOp> var_handle_ops) {
  std::vector<std::string> names;
  names.reserve(var_handle_ops.size());
  for (auto var_handle_op : var_handle_ops)
    names.push_back(GetVariableName(var_handle_op));
  return names;
}

tensorflow::Var* GetVariableFromSession(mlir::TF::VarHandleOp var_handle_op,

  if (!attr) return success();

  // ID pairs are separated by `,`.
  llvm::SmallVector<std::string, 8> str_list =
      absl::StrSplit(attr.getValue().str(), ',', absl::SkipWhitespace());
  id_pairs.reserve(str_list.size());
  for (const std::string& str : str_list) {
    // IDs of one pair are separated by `:`.
    llvm::SmallVector<std::string, 8> id_pair = absl::StrSplit(str, ':');

    // Check for malformed IDs.

  std::size_t dim_size = type.getDimSize(quantization_dim_);
  if (dim_size != scales_.size()) {
    return {};
  }
  SmallVector<UniformQuantizedValueConverter, 4> converters;
  converters.reserve(dim_size);
  for (int i = 0, e = dim_size; i != e; ++i) {
    converters.push_back(getPerChunkConverter(i));
  }

  // Scan the elements of the dense elements attributes and quantize them by