class_docstr: A pipeline model that exposes a sequence of models as a single model,
  It requires a set of inputs, a sequence of other models and a set of outputs.
  In this case the pipeline itself behaves as a classification model by designating
  a discrete categorical output feature as its 'predicted feature'.
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docstr: Create a set of pipeline models given a set of model specs.  The last
  model in this list must be a classifier model.
  
  Parameters
  ----------
  input_features: [list of 2-tuples]
      Name(s) of the input features, given as a list of `('name', datatype)`
      tuples.  The datatypes entry can be any of the data types defined in the
      :py:mod:`models.datatypes` module.
  
  class_labels: [list]
      A list of string or integer class labels to use in making predictions.
      This list must match the class labels in the model outputting the categorical
      predictedFeatureName
  
  output_features: [list]
      A string or a list of two strings specifying the names of the two
      output features, the first being a class label corresponding
      to the class with the highest predicted score, and the second being
      a dictionary mapping each class to its score. If `output_features`
      is a string, it specifies the predicted class label and the class
      scores is set to the default value of `"classProbability."`
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code: def __init__(
      self,
      input_features,
      class_labels,
      output_features=None,
      training_features=None,
  ):
      """
      Create a set of pipeline models given a set of model specs.  The last
      model in this list must be a classifier model.
  
      Parameters
      ----------
      input_features: [list of 2-tuples]
          Name(s) of the input features, given as a list of `('name', datatype)`
          tuples.  The datatypes entry can be any of the data types defined in the
          :py:mod:`models.datatypes` module.
  
      class_labels: [list]
          A list of string or integer class labels to use in making predictions.
          This list must match the class labels in the model outputting the categorical
          predictedFeatureName
  
      output_features: [list]
          A string or a list of two strings specifying the names of the two
          output features, the first being a class label corresponding
          to the class with the highest predicted score, and the second being
          a dictionary mapping each class to its score. If `output_features`
          is a string, it specifies the predicted class label and the class
          scores is set to the default value of `"classProbability."`
  
      """
  
      output_features = _feature_management.process_or_validate_classifier_output_features(
          output_features, class_labels
      )
  
      spec = _Model_pb2.Model()
      spec.specificationVersion = _SPECIFICATION_VERSION
      spec = set_classifier_interface_params(
          spec,
          input_features,
          class_labels,
          "pipelineClassifier",
          output_features,
          training_features,
      )
  
      # Access this to declare it as a pipeline
      spec.pipelineClassifier
  
      # Save as a member variable
      self.spec = spec
  
imports: from ..None import SPECIFICATION_VERSION
  from ..proto import Model_pb2
  from .None import _feature_management
  from .None import model
  from ._interface_management import set_classifier_interface_params, set_regressor_interface_params, set_training_features, set_transform_interface_params
label_desc: Use image node_id 1 to create a new PipelineClassifier object from inherited base classes: Pipeline with example: obj = PipelineClassifier(input_features, class_labels, output_features, training_features)