class_docstr: Model fitted by StandardScaler.
  
  .. versionadded:: 3.5.0
class_docstr_tok: ["Model","fitted","by","StandardScaler",".","..","versionadded",":",":","3.5.0"]
code: def __init__(
      self,
      mean_values: Optional["np.ndarray"] = None,
      std_values: Optional["np.ndarray"] = None,
      n_samples_seen: Optional[int] = None,
  ) -> None:
      super().__init__()
      self.mean_values = mean_values
      self.std_values = std_values
      if std_values is not None:
          # if scale value is zero, replace it with 1.0 (for preventing division by zero)
          self.scale_values = np.where(
              std_values == 0.0, 1.0, std_values
          )
      self.n_samples_seen = n_samples_seen
  
imports: import pickle
  from typing import Any, Union, List, Tuple, Callable, Dict, Optional
  import numpy
  import pandas
  from pyspark import keyword_only
  from pyspark.sql import DataFrame
  from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasFeatureSizes, HasHandleInvalid, Param, Params, TypeConverters
  from pyspark.ml.connect.base import Estimator, Model, Transformer
  from pyspark.ml.connect.io_utils import ParamsReadWrite, CoreModelReadWrite
  from pyspark.ml.connect.summarizer import summarize_dataframe
label_desc: Use image node_id 1 to create a new StandardScalerModel object from inherited base classes: Model, HasInputCol, HasOutputCol, ParamsReadWrite, CoreModelReadWrite with example: obj = StandardScalerModel(mean_values, std_values, n_samples_seen)