class_docstr: A Pipeline object chains transformers and a detector sequentially.
  
  Parameters
  ----------
  steps: list of 2-tuples (str, object)
      Components of this pipeline. Each 2-tuple represents a step in the
      pipeline (step name, model object).
  
  Examples
  --------
  >>> steps = [('moving average', RollingAggregate(agg='mean', window=10)),
               ('filter quantile 0.99', QuantileAD(high=0.99))]
  >>> myPipeline = Pipeline(steps)
docstr: Train models in pipeline sequentially, and transform time series
  along pipeline.
  
  Parameters
  ----------
  ts: pandas Series or DataFrame
      Time series to be transformed.
  
  skip_fit: list, optional
      Models to skip training. This could be used when pipeline contains
      models that are already trained by the same time series, and re-
      training would be time consuming. It must be a list of strings
      where each element is a model name. Default: None.
  
  return_intermediate: bool, optional
      Whether to return intermediate results. Default: False.
  
  Returns
  -------
  pandas Series, pandas DataFrame, or dict
      Transformed time series.
  
      - If return_intermediate=False, return transformed series, i.e.
        result from last transformer;
      - If return_intermediate=True, return results of all models in
        pipeline as a dict where each item represents the result of a
        model.
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code: def fit_transform(
      self,
      ts: Union[pd.Series, pd.DataFrame],
      skip_fit: Optional[List[str]] = None,
      return_intermediate: bool = False,
  ) -> Union[
      Union[pd.Series, pd.DataFrame],
      Dict[str, Union[pd.Series, pd.DataFrame]],
  ]:
      """Train models in pipeline sequentially, and transform time series
      along pipeline.
  
      Parameters
      ----------
      ts: pandas Series or DataFrame
          Time series to be transformed.
  
      skip_fit: list, optional
          Models to skip training. This could be used when pipeline contains
          models that are already trained by the same time series, and re-
          training would be time consuming. It must be a list of strings
          where each element is a model name. Default: None.
  
      return_intermediate: bool, optional
          Whether to return intermediate results. Default: False.
  
      Returns
      -------
      pandas Series, pandas DataFrame, or dict
          Transformed time series.
  
          - If return_intermediate=False, return transformed series, i.e.
            result from last transformer;
          - If return_intermediate=True, return results of all models in
            pipeline as a dict where each item represents the result of a
            model.
  
      """
      self._update_internal_pipenet()
      return self._pipenet.fit_transform(
          ts=ts,
          skip_fit=skip_fit,
          return_intermediate=return_intermediate,
      )
  
imports: from collections import OrderedDict
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import matplotlib.pyplot
  import pandas
  from matplotlib.collections import PatchCollection
  from matplotlib.lines import Line2D
  from matplotlib.patches import Circle
  from tabulate import tabulate
  from .._aggregator_base import _Aggregator
  from .._base import _Model, _TrainableModel
  from .._detector_base import _NonTrainableUnivariateDetector, _TrainableMultivariateDetector, _TrainableUnivariateDetector
  from .._transformer_base import _NonTrainableMultivariateTransformer, _NonTrainableUnivariateTransformer, _TrainableMultivariateTransformer, _TrainableUnivariateTransformer
  from ..metrics import f1_score, iou, precision, recall
label_desc: Use image node_id 7 for calling the Pipeline obj's underlying member method code with example usage: obj.fit_transform(ts, skip_fit, return_intermediate) and returns: self