class_docstr: Calculates the representations by passing the features through a single-layer ReLU neural network.
  :param relu_size: int or None
  The number of nodes in the ReLU layer. If None, the layer will be of size 4*n_components.
code: def connect_representation_graph(
      self, tf_features, n_components, n_features, node_name_ending
  ):
      # Infer ReLU layer size if necessary
      if self.relu_size is None:
          relu_size = 4 * n_components
      else:
          relu_size = self.relu_size
  
      # Create variable nodes
      tf_relu_weights = tf.Variable(
          tf.random_normal([n_features, relu_size], stddev=0.5),
          name="relu_weights_{}".format(node_name_ending),
      )
      tf_relu_biases = tf.Variable(
          tf.zeros([1, relu_size]),
          name="relu_biases_{}".format(node_name_ending),
      )
      tf_linear_weights = tf.Variable(
          tf.random_normal([relu_size, n_components], stddev=0.5),
          name="linear_weights_{}".format(node_name_ending),
      )
  
      # Create ReLU layer
      tf_relu = tf.nn.relu(
          tf.add(
              tf.sparse_tensor_dense_matmul(
                  tf_features, tf_relu_weights
              ),
              tf_relu_biases,
          )
      )
      tf_repr = tf.matmul(tf_relu, tf_linear_weights)
  
      # Return repr layer and variables
      return tf_repr, [
          tf_relu_weights,
          tf_linear_weights,
          tf_relu_biases,
      ]
  
imports: import abc
  import tensorflow
label_desc: Use image node_id 2 for calling the ReLURepresentationGraph obj's underlying member method code with example usage: obj.connect_representation_graph(tf_features, n_components, n_features, node_name_ending) and returns: tf_repr,  list