parameters: (self,
                 dense_float_column,
                 l1_regularization,
                 l2_regularization,
                 tree_complexity_regularization,
                 min_node_weight,
                 feature_column_group_id,
                 epsilon,
                 num_quantiles,
                 gradient_shape,
                 hessian_shape,
                 multiclass_strategy,
                 init_stamp_token=0,
                 name=None)
docstring: Initialize the internal state for this split handler.
  
      Args:
        dense_float_column: A `Tensor` column 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.
        epsilon: A float, the error bound for quantile computation.
        num_quantiles: An int, the number of buckets to create from the histogram.
        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.
        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,
                 dense_float_column,
                 l1_regularization,
                 l2_regularization,
                 tree_complexity_regularization,
                 min_node_weight,
                 feature_column_group_id,
                 epsilon,
                 num_quantiles,
                 gradient_shape,
                 hessian_shape,
                 multiclass_strategy,
                 init_stamp_token=0,
                 name=None):
      """Initialize the internal state for this split handler.
  
      Args:
        dense_float_column: A `Tensor` column 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.
        epsilon: A float, the error bound for quantile computation.
        num_quantiles: An int, the number of buckets to create from the histogram.
        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.
        name: An optional handler name.
      """
      super(DenseSplitHandler, 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,
          epsilon=epsilon,
          num_quantiles=num_quantiles,
          init_stamp_token=init_stamp_token,
          name=name,
          gradient_shape=gradient_shape,
          hessian_shape=hessian_shape,
          multiclass_strategy=multiclass_strategy)
      self._dense_float_column = dense_float_column
      # Register dense_make_stats_update function as an Op to the graph.
      g = ops.get_default_graph()
      dense_make_stats_update.add_to_graph(g)
function_tokens: def __init__ ( self , dense_float_column , l1_regularization , l2_regularization , tree_complexity_regularization , min_node_weight , feature_column_group_id , epsilon , num_quantiles , gradient_shape , hessian_shape , multiclass_strategy , init_stamp_token = 0 , name = None ) : super ( DenseSplitHandler , 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 , epsilon = epsilon , num_quantiles = num_quantiles , init_stamp_token = init_stamp_token , name = name , gradient_shape = gradient_shape , hessian_shape = hessian_shape , multiclass_strategy = multiclass_strategy ) self . _dense_float_column = dense_float_column # Register dense_make_stats_update function as an Op to the graph. g = ops . get_default_graph ( ) dense_make_stats_update . add_to_graph ( g )