code: def add_ops(self, net):
      version_info = get_current_scope().get(
          get_sparse_lookup_predictor_version.__name__,
          {"version": "fp32"},
      )
      lookup_table_blob_size = self.shape[0] * self.shape[1]
      version = get_sparse_lookup_predictor_version(
          version_info["version"],
          blob_size=lookup_table_blob_size,
          min_blob_size_4bits=(
              version_info["min_blob_size_4bits"]
              if "min_blob_size_4bits" in version_info
              else None
          ),
          embedding_dim=self.shape[1],
          sparse_feature_name=self.sparse_key,
      )
  
      # TODO(amalevich): Layer should not be responsible for decision about
      # quantization.
      if not self.support_8bit() and version in {
          "uint8rowwise",
          "fused_uint8rowwise",
          "fused_uint4rowwise",
      }:
          version = "fp16"
  
      self._add_ops(net, version, is_train=False)
  
imports: from caffe2.python.optimizer import FP16_ENGINES, Optimizer
  from caffe2.python.helpers.arg_scope import get_current_scope
  from caffe2.python import schema
  from caffe2.python.layers.layers import get_categorical_limit, get_key, IdList, IdScoreList, IdListWithEvicted, IdScoreListWithEvicted, LayerPsParam, ModelLayer, almost_equal_schemas
  import collections
  import functools
  import logging
  import math
  import numpy
  import operator
label_desc: Use image node_id 13 for calling the SparseLookup obj's underlying member method code with example usage: obj.add_ops(net) without return types