class_docstr: Calculates the representation by passing the features through a linear embedding. Embeddings are L2 normalized,
  meaning all embeddings have equal magnitude. This can be useful as a user representation in mixture-of-tastes
  models, preventing one taste from having a much larger magnitude than others and dominating the recommendations.
code: def connect_representation_graph(
      self, tf_features, n_components, n_features, node_name_ending
  ):
      tf_repr, weights_list = super(
          NormalizedLinearRepresentationGraph, self
      ).connect_representation_graph(
          tf_features=tf_features,
          n_components=n_components,
          n_features=n_features,
          node_name_ending=node_name_ending,
      )
      normalized_repr = tf.nn.l2_normalize(tf_repr, 1)
      return normalized_repr, weights_list
  
imports: import abc
  import tensorflow
label_desc: Use image node_id 1 for calling the NormalizedLinearRepresentationGraph obj's underlying member method code with example usage: obj.connect_representation_graph(tf_features, n_components, n_features, node_name_ending) and returns: normalized_repr,  weights_list