code: def parameter(
      input_var,
      length,
      initializer=tf.zeros_initializer(),
      dtype=tf.float32,
      trainable=True,
      name="parameter",
  ):
      """Parameter layer.
  
      Used as layer that could be broadcast to a certain shape to
      match with input variable during training.
  
      For recurrent usage, use garage.tf.models.recurrent_parameter().
  
      Example: A trainable parameter variable with shape (2,), it needs to be
      broadcasted to (32, 2) when applied to a batch with size 32.
  
      Args:
          input_var (tf.Tensor): Input tf.Tensor.
          length (int): Integer dimension of the variable.
          initializer (callable): Initializer of the variable. The function
              should return a tf.Tensor.
          dtype: Data type of the variable (default is tf.float32).
          trainable (bool): Whether the variable is trainable.
          name (str): Variable scope of the variable.
  
      Return:
          A tensor of the broadcasted variables.
      """
      with tf.compat.v1.variable_scope(name):
          p = tf.compat.v1.get_variable(
              "parameter",
              shape=(length,),
              dtype=dtype,
              initializer=initializer,
              trainable=trainable,
          )
          batch_dim = tf.shape(input_var)[0]
          broadcast_shape = tf.concat(
              axis=0, values=[[batch_dim], [length]]
          )
          p_broadcast = tf.broadcast_to(p, shape=broadcast_shape)
          return p_broadcast
  
label_desc: Use image node_id 1 for calling a global function with example usage: parameter(input_var, length, initializer, dtype, trainable, name) and returns: p_broadcast