class_docstr: Represents a feature column abstraction.
  
  WARNING: Do not subclass this layer unless you know what you are doing:
  the API is subject to future changes.
  
  To distinguish between the concept of a feature family and a specific binary
  feature within a family, we refer to a feature family like "country" as a
  feature column. For example, we can have a feature in a `tf.Example` format:
    {key: "country",  value: [ "US" ]}
  In this example the value of feature is "US" and "country" refers to the
  column of the feature.
  
  This class is an abstract class. Users should not create instances of this.
docstr: Returns a list of immediate raw feature and FeatureColumn dependencies.
  
  For example:
  # For the following feature columns
  a = numeric_column('f1')
  c = crossed_column(a, 'f2')
  # The expected parents are:
  a.parents = ['f1']
  c.parents = [a, 'f2']
docstr_tok: ["Returns","a","list","of","immediate","raw","feature","and","FeatureColumn","dependencies",".","For","example",":","#","For","the","following","feature","columns","a","=","numeric_column","(","'f1","'",")","c","=","crossed_column","(","a",",","'f2","'",")","#","The","expected","parents","are",":","a.parents","=","[","'f1","'","]","c.parents","=","[","a",",","'f2","'","]"]
code: def parents(self):
      """Returns a list of immediate raw feature and FeatureColumn dependencies.
  
      For example:
      # For the following feature columns
      a = numeric_column('f1')
      c = crossed_column(a, 'f2')
      # The expected parents are:
      a.parents = ['f1']
      c.parents = [a, 'f2']
      """
      pass
  
file_path: tensorflow-upstream/tensorflow/python/feature_column/feature_column_v2_types.py
imports: import abc
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling the FeatureColumn obj's underlying member method code with example usage: obj.parents() without return types