parameters: (self,
                 sparse_int_column,
                 l1_regularization,
                 l2_regularization,
                 tree_complexity_regularization,
                 min_node_weight,
                 feature_column_group_id,
                 gradient_shape,
                 hessian_shape,
                 multiclass_strategy,
                 init_stamp_token=0,
                 loss_uses_sum_reduction=False,
                 weak_learner_type=learner_pb2.LearnerConfig.NORMAL_DECISION_TREE,
                 name=None)
docstring: Initialize the internal state for this split handler.
  
      Args:
        sparse_int_column: A `SparseTensor` column with int64 values associated
          with this handler.
        l1_regularization: L1 regularization applied for this split handler.
        l2_regularization: L2 regularization applied for this split handler.
        tree_complexity_regularization: Tree complexity regularization applied
            for this split handler.
        min_node_weight: Minimum sum of weights of examples in each partition to
            be considered for splitting.
        feature_column_group_id: Feature column group index.
        gradient_shape: A TensorShape, containing shape of gradients.
        hessian_shape: A TensorShape, containing shape of hessians.
        multiclass_strategy: Strategy describing how to treat multiclass problems.
        init_stamp_token: A tensor containing an scalar for initial stamp of the
           stamped objects.
        loss_uses_sum_reduction: A scalar boolean tensor that specifies whether
            SUM or MEAN reduction was used for the loss.
        weak_learner_type: Specifies the type of weak learner to use.
        name: An optional handler name.
docstring_summary: Initialize the internal state for this split handler.
docstring_tokens: Initialize the internal state for this split handler .
function: def __init__(self,
                 sparse_int_column,
                 l1_regularization,
                 l2_regularization,
                 tree_complexity_regularization,
                 min_node_weight,
                 feature_column_group_id,
                 gradient_shape,
                 hessian_shape,
                 multiclass_strategy,
                 init_stamp_token=0,
                 loss_uses_sum_reduction=False,
                 weak_learner_type=learner_pb2.LearnerConfig.NORMAL_DECISION_TREE,
                 name=None):
      """Initialize the internal state for this split handler.
  
      Args:
        sparse_int_column: A `SparseTensor` column with int64 values associated
          with this handler.
        l1_regularization: L1 regularization applied for this split handler.
        l2_regularization: L2 regularization applied for this split handler.
        tree_complexity_regularization: Tree complexity regularization applied
            for this split handler.
        min_node_weight: Minimum sum of weights of examples in each partition to
            be considered for splitting.
        feature_column_group_id: Feature column group index.
        gradient_shape: A TensorShape, containing shape of gradients.
        hessian_shape: A TensorShape, containing shape of hessians.
        multiclass_strategy: Strategy describing how to treat multiclass problems.
        init_stamp_token: A tensor containing an scalar for initial stamp of the
           stamped objects.
        loss_uses_sum_reduction: A scalar boolean tensor that specifies whether
            SUM or MEAN reduction was used for the loss.
        weak_learner_type: Specifies the type of weak learner to use.
        name: An optional handler name.
      """
      super(EqualitySplitHandler, self).__init__(
          l1_regularization=l1_regularization,
          l2_regularization=l2_regularization,
          tree_complexity_regularization=tree_complexity_regularization,
          min_node_weight=min_node_weight,
          feature_column_group_id=feature_column_group_id,
          gradient_shape=gradient_shape,
          hessian_shape=hessian_shape,
          multiclass_strategy=multiclass_strategy,
          loss_uses_sum_reduction=loss_uses_sum_reduction,
          name=name)
      self._stats_accumulator = stats_accumulator_ops.StatsAccumulator(
          init_stamp_token,
          gradient_shape,
          hessian_shape,
          name="StatsAccumulator/{}".format(self._name))
      self._sparse_int_column = sparse_int_column
      self._weak_learner_type = weak_learner_type
function_tokens: def __init__ ( self , sparse_int_column , l1_regularization , l2_regularization , tree_complexity_regularization , min_node_weight , feature_column_group_id , gradient_shape , hessian_shape , multiclass_strategy , init_stamp_token = 0 , loss_uses_sum_reduction = False , weak_learner_type = learner_pb2 . LearnerConfig . NORMAL_DECISION_TREE , name = None ) : super ( EqualitySplitHandler , self ) . __init__ ( l1_regularization = l1_regularization , l2_regularization = l2_regularization , tree_complexity_regularization = tree_complexity_regularization , min_node_weight = min_node_weight , feature_column_group_id = feature_column_group_id , gradient_shape = gradient_shape , hessian_shape = hessian_shape , multiclass_strategy = multiclass_strategy , loss_uses_sum_reduction = loss_uses_sum_reduction , name = name ) self . _stats_accumulator = stats_accumulator_ops . StatsAccumulator ( init_stamp_token , gradient_shape , hessian_shape , name = "StatsAccumulator/{}" . format ( self . _name ) ) self . _sparse_int_column = sparse_int_column self . _weak_learner_type = weak_learner_type