class_docstr: Uses the features as the representation. This representation graph learns weights for each feature.
code: def connect_representation_graph(
      self, tf_features, n_components, n_features, node_name_ending
  ):
      dense_repr, _ = super(
          WeightedFeaturePassThroughRepresentationGraph, self
      ).connect_representation_graph(
          tf_features=tf_features,
          n_components=n_components,
          n_features=n_features,
          node_name_ending=node_name_ending,
      )
  
      weights = tf.ones([1, n_components])
      weighted_repr = tf.multiply(dense_repr, weights)
      return weighted_repr, [weights]
  
imports: import abc
  import tensorflow
label_desc: Use image node_id 1 for calling the WeightedFeaturePassThroughRepresentationGraph obj's underlying member method code with example usage: obj.connect_representation_graph(tf_features, n_components, n_features, node_name_ending) and returns: weighted_repr,  list