step.

The tensors at corresponding positions in the three input lists
must have the same shape, i.e. rank 1 with dim_size() equal to the total
number of lookups into the table described by the corresponding table_id.
  }];

  let arguments = (ins
    Arg<Variadic<TF_I32OrI64Tensor>, [{A list of rank 1 Tensors specifying the training example and
feature to which the corresponding embedding_indices and aggregation_weights

		if podName == "" && namespace == "" {
			if pod, found := s.GetPodByCgroupfs(c.Name); found {
				podName = pod.Name
				namespace = pod.Namespace
			}
		}
		set := map[string]string{
			metrics.LabelID:    c.Name,
			metrics.LabelName:  name,
			metrics.LabelImage: image,
			"pod":              podName,
			"namespace":        namespace,
			"container":        containerName,
		}
		return set
	}

	EvGoUnblockLocal:    {"GoUnblockLocal", 7, true, []string{"g"}, nil},
	EvGoSysExitLocal:    {"GoSysExitLocal", 7, false, []string{"g", "ts"}, nil},
	EvGoStartLabel:      {"GoStartLabel", 8, false, []string{"g", "seq", "labelid"}, []string{"label"}},
	EvGoBlockGC:         {"GoBlockGC", 8, true, []string{}, nil},
	EvGCMarkAssistStart: {"GCMarkAssistStart", 9, true, []string{}, nil},
	EvGCMarkAssistDone:  {"GCMarkAssistDone", 9, false, []string{}, nil},

struct HashtableOptionsBuilder {
  typedef HashtableOptions Table;
  ::flatbuffers::FlatBufferBuilder &fbb_;
  ::flatbuffers::uoffset_t start_;
  void add_table_id(int32_t table_id) {
    fbb_.AddElement<int32_t>(HashtableOptions::VT_TABLE_ID, table_id, 0);
  }
  void add_key_dtype(tflite::TensorType key_dtype) {
    fbb_.AddElement<int8_t>(HashtableOptions::VT_KEY_DTYPE, static_cast<int8_t>(key_dtype), 0);
  }

Before using the table you will have to initialize it.  After initialization the
table will be immutable.
  }];

  let arguments = (ins
    I32Attr:$table_id,
    TypeAttr:$key_dtype,
    TypeAttr:$value_dtype
  );

  let results = (outs TFL_ResourceTensor:$out);

  let hasOptions = 1;
}