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'.
class_docstr_tok: ["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","'","."]
docstr: Set the training inputs of the network spec.
  
  Parameters
  ----------
  training_input: [tuple]
      List of training input names and type of the network.
docstr_tok: ["Set","the","training","inputs","of","the","network","spec",".","Parameters","--","--","--","--","--","training_input",":","[","tuple","]","List","of","training","input","names","and","type","of","the","network","."]
code: def set_training_input(self, training_input):
      """
      Set the training inputs of the network spec.
  
      Parameters
      ----------
      training_input: [tuple]
          List of training input names and type of the network.
      """
      spec = self.spec
      set_training_features(spec, training_input)
  
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 4 for calling the PipelineClassifier obj's underlying member method code with example usage: obj.set_training_input(training_input) without return types