auto sharding_attr = mlir::dyn_cast<StringAttr>(attr);
  if (!sharding_attr)
    return op->emitOpError(
        llvm::formatv(kBadStringArrayElementMsg, name, index));
  if (tensorflow::DecodeShardingAttribute(sharding_attr, *sharding_ptr)
          .failed()) {
    return op->emitOpError(llvm::formatv(kBadArrayElementMsg, name, index,
                                         sharding_attr.getValue(),

  if (!op->hasAttr(TF::kTpuReplicateAttr)) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "missing _tpu_replicate attr");
    return false;
  }
  auto opattr = op->getAttr(TF::kTpuReplicateAttr);
  if (opattr != attr) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "invalid _tpu_replicate attr.")

        return fetch.emitOpError()
               << "operand #" << i
               << " is a control type, can't be bound to a graph result";
      break;
    }
    if (i >= num_results)
      return fetch.emitOpError()
             << "operand #" << i << " does not have a graph results to bind";
    if (graph.getResult(i).getType() != operand.getType()) {
      return fetch.emitOpError()

  if (!matchPattern(group_assignment_value, m_Constant(&group_assignment))) {
    return op->emitOpError() << "expects constant group_assignment";
  }
  replica_groups = mlir::cast<DenseIntElementsAttr>(
      hlo::convertElementsAttr(group_assignment, builder.getIntegerType(64)));
  if (replica_groups.getType().getRank() != 2) {
    return op->emitOpError() << "group_assignment should have rank 2, got "

  if (it == buffer_to_size->end()) {
    return scatter.emitOpError("unknown tensor list");
  }
  auto buffer = scatter.getInputHandle();
  OpBuilder builder(scatter);
  auto indices_type =
      mlir::cast<RankedTensorType>(scatter.getIndices().getType());
  if (!indices_type) return scatter.emitOpError("unranked indices shape");
  auto shape_type = RankedTensorType::get({2}, builder.getIntegerType(32));

    if (IfOp if_op = llvm::dyn_cast<IfOp>(op)) {
      if (failed(ConvertIfOp(if_op))) {
        op->emitOpError() << "failed to convert to region form";
        return WalkResult::interrupt();
      }
    } else if (CaseOp case_op = llvm::dyn_cast<CaseOp>(op)) {
      if (failed(ConvertCaseOp(case_op))) {
        op->emitOpError() << "failed to convert to region form";
        return WalkResult::interrupt();
      }

  for (OpType op : func.getOps<OpType>()) {
    if (found_op != nullptr) {
      func.emitOpError() << "number of " << found_op.getOperationName()
                         << " in loop body is not 1";
      return LogicalResult::failure();
    }
    if (GetReplicationAttr(op) != replication_attr) {
      op.emitOpError() << "is not part of the replication region "

        if (!backend_config) {
          op->emitOpError()
              << "is missing attribute '" << kTfBackendConfigAttrName << "'";
          return WalkResult::interrupt();
        }

        auto called_index_attr = mlir::dyn_cast_or_null<IntegerAttr>(
            backend_config.get(kCalledIndexAttrName));
        if (!called_index_attr) {
          op->emitOpError()

        return mlir::dyn_cast_or_null<StringAttr>(attr);
      });
      if (!is_device_string)
        return op.emitOpError() << "expects 'devices' to be a consists of "
                                   "string list as values.";

      if (device_list.size() != n)
        return op.emitOpError()
               << "expects number of devices (" << device_list.size()
               << ") to be equal to 'n' (" << n << ")";

        if (func_arg.getOwner() != &block)
          return read_op.emitOpError(kResourceFunctionMsg);

        ResourceInfo& resource_info = resources[func_arg.getArgNumber()];
        resource_info.read = true;
        read_op.getValue().replaceAllUsesWith(resource_info.live_value);
      } else {
        return read_op.emitOpError(kInvalidResourceMsg);
      }

      read_op.erase();