parameters: (self, key)
return_statement: return transformed
docstring: Returns a `Tensor` for the given key.
  
      A `str` key is used to access a base feature (not-transformed). When a
      `_FeatureColumn` is passed, the transformed feature is returned if it
      already exists, otherwise the given `_FeatureColumn` is asked to provide its
      transformed output, which is then cached.
  
      Args:
        key: a `str` or a `_FeatureColumn`.
  
      Returns:
        The transformed `Tensor` corresponding to the `key`.
  
      Raises:
        ValueError: if key is not found or a transformed `Tensor` cannot be
          computed.
docstring_summary: Returns a `Tensor` for the given key.
docstring_tokens: Returns a Tensor for the given key .
function: def get(self, key):
      """Returns a `Tensor` for the given key.
  
      A `str` key is used to access a base feature (not-transformed). When a
      `_FeatureColumn` is passed, the transformed feature is returned if it
      already exists, otherwise the given `_FeatureColumn` is asked to provide its
      transformed output, which is then cached.
  
      Args:
        key: a `str` or a `_FeatureColumn`.
  
      Returns:
        The transformed `Tensor` corresponding to the `key`.
  
      Raises:
        ValueError: if key is not found or a transformed `Tensor` cannot be
          computed.
      """
      if key in self._feature_tensors:
        # FeatureColumn is already transformed or converted.
        return self._feature_tensors[key]
  
      if key in self._features:
        feature_tensor = self._get_raw_feature_as_tensor(key)
        self._feature_tensors[key] = feature_tensor
        return feature_tensor
  
      if isinstance(key, six.string_types):
        raise ValueError('Feature {} is not in features dictionary.'.format(key))
  
      if not isinstance(key, _FeatureColumn):
        raise TypeError('"key" must be either a "str" or "_FeatureColumn". '
                        'Provided: {}'.format(key))
  
      column = key
      logging.debug('Transforming feature_column %s.', column)
      transformed = column._transform_feature(self)  # pylint: disable=protected-access
      if transformed is None:
        raise ValueError('Column {} is not supported.'.format(column.name))
      self._feature_tensors[column] = transformed
      return transformed
function_tokens: def get ( self , key ) : if key in self . _feature_tensors : # FeatureColumn is already transformed or converted. return self . _feature_tensors [ key ] if key in self . _features : feature_tensor = self . _get_raw_feature_as_tensor ( key ) self . _feature_tensors [ key ] = feature_tensor return feature_tensor if isinstance ( key , six . string_types ) : raise ValueError ( 'Feature {} is not in features dictionary.' . format ( key ) ) if not isinstance ( key , _FeatureColumn ) : raise TypeError ( '"key" must be either a "str" or "_FeatureColumn". ' 'Provided: {}' . format ( key ) ) column = key logging . debug ( 'Transforming feature_column %s.' , column ) transformed = column . _transform_feature ( self ) # pylint: disable=protected-access if transformed is None : raise ValueError ( 'Column {} is not supported.' . format ( column . name ) ) self . _feature_tensors [ column ] = transformed return transformed