code: def __init__(
      self,
      col_name: str,
      code_len: int,
      start_id: int,
      fillall: bool = True,
      base: int = 100,
      hasnan: bool = True,
      transform: str = "quantile",
  ):
      super().__init__(
          col_name, code_len, start_id, fillall, base, hasnan
      )
      if transform == "yeo-johnson":
          self.scaler = PowerTransformer(standardize=True)
      elif transform == "quantile":
          self.scaler = QuantileTransformer(
              output_distribution="uniform", n_quantiles=100
          )
      elif transform == "robust":
          self.scaler = RobustScaler()
      else:
          raise ValueError(
              'Supported data transformations are "yeo-johnson", "quantile", and "robust"'
          )
  
imports: import math
  from typing import Dict, List, Tuple
  import numpy
  from numpy import ndarray
  from sklearn.preprocessing import PowerTransformer, QuantileTransformer, RobustScaler
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new FloatCode object from inherited base classes: IntCode with example: obj = FloatCode(col_name, code_len, start_id, fillall, base, hasnan, transform)