code: def initial_cell_state_from_embedding(cell, z, name=None):
      """Computes an initial RNN `cell` state from an embedding, `z`."""
      flat_state_sizes = tf.nest.flatten(cell.state_size)
      return tf.nest.pack_sequence_as(
          cell.zero_state(batch_size=z.shape[0], dtype=tf.float32),
          tf.split(
              tf.layers.dense(
                  z,
                  sum(flat_state_sizes),
                  activation=tf.tanh,
                  kernel_initializer=tf.random_normal_initializer(
                      stddev=0.001
                  ),
                  name=name,
              ),
              flat_state_sizes,
              axis=1,
          ),
      )
  
imports: import collections
  from magenta.contrib import rnn
  from magenta.contrib import seq2seq
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling a global function with example usage: initial_cell_state_from_embedding(cell, z, name) and returns: tf