class_docstr: The element stored in the quantization hash.
  
  Each slot keeps the mean values of the numerical feature, as well as the variance
  and mean of the target.
class_docstr_tok: ["The","element","stored","in","the","quantization","hash",".","Each","slot","keeps","the","mean","values","of","the","numerical","feature",",","as","well","as","the","variance","and","mean","of","the","target","."]
code: def __init__(
      self,
      x: float,
      y=typing.Union[float, "utils.VectorDict"],
      weight: float = 1.0,
  ):
      self.x_stats = stats.Mean()
      self.x_stats.update(x, weight)
  
      self.y_stats: stats.Var | utils.VectorDict
  
      self._update_estimator: typing.Callable[
          [float | utils.VectorDict, float], None
      ]
      self.is_single_target = True
  
      self._init_estimator(y)
      self._update_estimator(y, weight)
  
imports: from __future__ import annotations
  import functools
  import math
  import typing
  from river import stats
  from ..utils import BranchFactory
  from .base import Splitter
label_desc: Use image node_id 1 to create a new Slot object  with example: obj = Slot(x, y, weight)