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 whether this FeatureColumn is fully conformant to the new API.
  
  This is needed for composition type cases where an EmbeddingColumn etc.
  might take in old categorical columns as input and then we want to use the
  old API.
docstr_tok: ["Returns","whether","this","FeatureColumn","is","fully","conformant","to","the","new","API",".","This","is","needed","for","composition","type","cases","where","an","EmbeddingColumn","etc",".","might","take","in","old","categorical","columns","as","input","and","then","we","want","to","use","the","old","API","."]
code: def _is_v2_column(self):
      """Returns whether this FeatureColumn is fully conformant to the new API.
  
      This is needed for composition type cases where an EmbeddingColumn etc.
      might take in old categorical columns as input and then we want to use the
      old API.
      """
      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 7 for calling the FeatureColumn obj's underlying member method code with example usage: obj._is_v2_column() without return types