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: Get parameters of models in pipeline.
  
  Returns
  -------
  dict
      A dictionary of model name and model parameters.
docstr_tok: ["Get","parameters","of","models","in","pipeline",".","Returns","--","--","--","-","dict","A","dictionary","of","model","name","and","model","parameters","."]
code: def get_params(self) -> Dict[str, Dict[str, Any]]:
      """Get parameters of models in pipeline.
  
      Returns
      -------
      dict
          A dictionary of model name and model parameters.
  
      """
      self._update_internal_pipenet()
      return self._pipenet.get_params()
  
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 9 for calling the Pipeline obj's underlying member method code with example usage: obj.get_params() and returns: self