code: def _get_default_init_op(self):
      scale = math.sqrt(
          self.uniform_weight_init_scale_numerator / self.input_dim
      )
  
      if self.trainer_version == "fp32":
          default_weight_init = (
              "UniformFill",
              {"min": -scale, "max": scale},
          )
      elif self.trainer_version == "fp16":
          default_weight_init = (
              "Float16UniformFill",
              {"min": -scale, "max": scale},
          )
      else:
          raise NotImplementedError(
              "Train version {} is not currently supported for sparse feature {}".format(
                  trainer_version, self.sparse_key
              )
          )
  
      return default_weight_init
  
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 6 for calling the SparseLookup obj's underlying member method code with example usage: obj._get_default_init_op() and returns: default_weight_init