class_docstr: Class for application of ``Scaling operation`` on data,
  where only not encoded features (were not converted from categorical using
  ``OneHot encoding``) are used
  
  Args:
      params: OperationParameters with the arguments
class_docstr_tok: ["Class","for","application","of","``","Scaling","operation","``","on","data",",","where","only","not","encoded","features","(","were","not","converted","from","categorical","using","``","OneHot","encoding","``",")","are","used","Args",":","params",":","OperationParameters","with","the","arguments"]
code: def __init__(self, params: Optional[OperationParameters]):
      super().__init__(params)
      self.operation = StandardScaler(**self.params.to_dict())
  
imports: import random
  from typing import Optional
  import numpy
  import pandas
  from sklearn.decomposition import FastICA, KernelPCA, PCA
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures, StandardScaler
  from fedot.core.data.data import InputData, OutputData, data_type_is_table
  from fedot.core.data.data_preprocessing import convert_into_column, data_has_categorical_features, divide_data_categorical_numerical, find_categorical_columns, replace_inf_with_nans
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import DataOperationImplementation, EncodedInvariantImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.preprocessing.data_types import TYPE_TO_ID
label_desc: Use image node_id 1 to create a new ScalingImplementation object from inherited base classes: EncodedInvariantImplementation with example: obj = ScalingImplementation(params)