class_docstr: Transformer base class.
class_docstr_tok: ["Transformer","base","class","."]
docstr: Magic * method, return (left) concatenated TransformerPipeline.
  
  Implemented for `other` being a transformer, otherwise returns `NotImplemented`.
  
  Parameters
  ----------
  other: `sktime` transformer, must inherit from BaseTransformer
      otherwise, `NotImplemented` is returned
  
  Returns
  -------
  TransformerPipeline object, concatenation of `other` (first) with `self` (last).
      not nested, contains only non-TransformerPipeline `sktime` transformers
code: def __rmul__(self, other):
      """Magic * method, return (left) concatenated TransformerPipeline.
  
      Implemented for `other` being a transformer, otherwise returns `NotImplemented`.
  
      Parameters
      ----------
      other: `sktime` transformer, must inherit from BaseTransformer
          otherwise, `NotImplemented` is returned
  
      Returns
      -------
      TransformerPipeline object, concatenation of `other` (first) with `self` (last).
          not nested, contains only non-TransformerPipeline `sktime` transformers
      """
      from sktime.transformations.compose import TransformerPipeline
  
      # we wrap self in a pipeline, and concatenate with the other
      #   the TransformerPipeline does the rest, e.g., case distinctions on other
      if isinstance(other, BaseTransformer) or is_sklearn_transformer(
          other
      ):
          self_as_pipeline = TransformerPipeline(steps=[self])
          return other * self_as_pipeline
      else:
          return NotImplemented
  
imports: from itertools import product
  from typing import Union
  import numpy
  import pandas
  from sktime.base import BaseEstimator
  from sktime.datatypes import VectorizedDF, check_is_error_msg, check_is_mtype, check_is_scitype, convert, convert_to, mtype_to_scitype, update_data
  from sktime.datatypes._series_as_panel import convert_to_scitype
  from sktime.utils.sklearn import is_sklearn_classifier, is_sklearn_clusterer, is_sklearn_regressor, is_sklearn_transformer
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 3 for calling the BaseTransformer obj's underlying member method code with example usage: obj.__rmul__(other) and returns: unknown, NotImplemented