erasureSetWriteTolerance     = "write_tolerance"
	erasureSetReadHealth         = "read_health"
	erasureSetWriteHealth        = "write_health"
)

const (
	poolIDL = "pool_id"
	setIDL  = "set_id"
)

var (
	erasureSetOverallWriteQuorumMD = NewGaugeMD(erasureSetOverallWriteQuorum,
		"Overall write quorum across pools and sets")
	erasureSetOverallHealthMD = NewGaugeMD(erasureSetOverallHealth,

				continue
			}
			// Entry from internal/poll.
			mode := op.mode
			if mode != 'r' && mode != 'w' {
				println("runtime: GetQueuedCompletionStatusEx returned net_op with invalid mode=", mode)
				throw("runtime: netpoll failed")
			}
			delta += netpollready(&toRun, op.pd, mode)
		case netpollSourceBreak:
			netpollWakeSig.Store(0)
			if delay == 0 {

                             const string& node_name, int num_constant_args,
                             int num_resource_args, Node** result) {
  NodeDef call_node;
  call_node.set_name(node_name);
  call_node.set_op(callee_name);
  AddNodeAttr(kXlaCompiledKernelAttr, true, &call_node);
  AddNodeAttr(kXlaNumConstantArgsAttr, num_constant_args, &call_node);
  AddNodeAttr(kXlaNumResourceArgsAttr, num_resource_args, &call_node);

                                 NodeDef* node_def) {
  const NameAttrList* name_attr;
  TF_RETURN_IF_ERROR(GetNodeAttr(node.attrs(), attr_name, &name_attr));
  node_def->set_op(name_attr->name());
  *(node_def->mutable_attr()) = name_attr->attr();
  return absl::OkStatus();
}

absl::StatusOr<std::vector<NodeDef>> MakeCallNodesFromAttribute(
    const Node& node, absl::string_view attr_name,

        TensorShapeProto shape_proto;
        context->ShapeHandleToProto(handle, &shape_proto);
        if (!shape_proto.unknown_rank()) {
          NodeDef const_def;
          const_def.set_op("Const");
          Node* var_node;
          TF_RETURN_IF_ERROR(n->input_node(0, &var_node));
          const_def.set_name(
              graph->NewName(absl::StrCat("var_shape_", var_node->name())));

      attr->contains("DstT") &&
      attr->at("SrcT").type() == attr->at("DstT").type() &&
      attr->contains("Truncate") && !attr->at("Truncate").b()) {
    node_def->set_op("Identity");
    attr->insert({{"T", attr->at("SrcT")}});
    attr->erase("SrcT");
    attr->erase("DstT");
    attr->erase("Truncate");
  }
}

}  // namespace

Status GetAttrValuesFromOperation(

          if (TF::TensorListSetItemOp set_op =
                  llvm::dyn_cast<TF::TensorListSetItemOp>(use.getOwner())) {
            element_shape = rewriter.create<TF::ShapeOp>(
                op.getLoc(),
                tensorflow::GetTypeFromTFTensorShape({-1}, shape_dtype),
                set_op.getItem());
            element_shape_acquired = true;

  node_def->set_op(FunctionLibraryDefinition::kRetOp);
  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(
      mlir::cast<mlir::TensorType>(operand.getType()).getElementType(),
      &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;
  AttrValue index_attr;
  index_attr.set_i(index);

  node_def->set_op(FunctionLibraryDefinition::kRetOp);
  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(
      mlir::cast<mlir::TensorType>(operand.getType()).getElementType(),
      &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;
  AttrValue index_attr;
  index_attr.set_i(index);

    const absl::flat_hash_map<absl::string_view, int>& histogram) {
  for (const auto& pair : histogram) {
    XlaAutoClusteringSummary::OpAndCount* new_entry = result->Add();
    new_entry->set_op(std::string(pair.first));
    new_entry->set_count(pair.second);
  }

  absl::c_sort(*result, [](const XlaAutoClusteringSummary::OpAndCount& a,
                           const XlaAutoClusteringSummary::OpAndCount& b) {