code: def make_linear_network(w_name=None, b_name=None):
      # Define the inputs.
      x_data = tf.placeholder(tf.float32, shape=[100])
      y_data = tf.placeholder(tf.float32, shape=[100])
      # Define the weights and computation.
      w = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name=w_name)
      b = tf.Variable(tf.zeros([1]), name=b_name)
      y = w * x_data + b
      # Return the loss and weight initializer.
      return (
          tf.reduce_mean(tf.square(y - y_data)),
          tf.global_variables_initializer(),
          x_data,
          y_data,
      )
  
imports: from numpy.testing import assert_almost_equal
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 1 for calling a global function with example usage: make_linear_network(w_name, b_name) and returns: tf,  tf,  x_data,  y_data