code: def super_linear(
      x,
      output_size,
      scope=None,
      reuse=False,
      init_w="ortho",
      weight_start=0.0,
      use_bias=True,
      bias_start=0.0,
      input_size=None,
  ):
      """Performs linear operation. Uses ortho init defined earlier."""
      shape = x.get_shape().as_list()
      with tf.variable_scope(scope or "linear"):
          if reuse:
              tf.get_variable_scope().reuse_variables()
  
          w_init = None  # uniform
          if input_size is None:
              x_size = shape[1]
          else:
              x_size = input_size
          if init_w == "zeros":
              w_init = tf.constant_initializer(0.0)
          elif init_w == "constant":
              w_init = tf.constant_initializer(weight_start)
          elif init_w == "gaussian":
              w_init = tf.random_normal_initializer(stddev=weight_start)
          elif init_w == "ortho":
              w_init = lstm_ortho_initializer(1.0)
  
          w = tf.get_variable(
              "super_linear_w",
              [x_size, output_size],
              tf.float32,
              initializer=w_init,
          )
          if use_bias:
              b = tf.get_variable(
                  "super_linear_b",
                  [output_size],
                  tf.float32,
                  initializer=tf.constant_initializer(bias_start),
              )
              return tf.matmul(x, w) + b
          return tf.matmul(x, w)
  
imports: import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 7 for calling a global function with example usage: super_linear(x, output_size, scope, reuse, init_w, weight_start, use_bias, bias_start, input_size) and returns: tf, unknown