text: import numpy as np
  import tensorflow as tf
  from mpi4py import MPI
  from wcsac.utils.mpi_tools import broadcast
  
  
  def flat_concat(xs):
      return tf.concat([tf.reshape(x,(-1,)) for x in xs], axis=0)
  
  def assign_params_from_flat(x, params):
      flat_size = lambda p : int(np.prod(p.shape.as_list())) # the 'int' is important for scalars
      splits = tf.split(x, [flat_size(p) for p in params])
      new_params = [tf.reshape(p_new, p.shape) for p, p_new in zip(params, splits)]
      return tf.group([tf.assign(p, p_new) for p, p_new in zip(params, new_params)])
  
  def sync_params(params):
      get_params = flat_concat(params)
      def _broadcast(x):
          broadcast(x)
          return x
      synced_params = tf.py_func(_broadcast, [get_params], tf.float32)
      return assign_params_from_flat(synced_params, params)
  
  def sync_all_params():
      """Sync all tf variables across MPI processes."""
      return sync_params(tf.global_variables())
  
  
  class MpiAdamOptimizer(tf.train.AdamOptimizer):
      """
      Adam optimizer that averages gradients across MPI processes.
  
      The compute_gradients method is taken from Baselines `MpiAdamOptimizer`_. 
      For documentation on method arguments, see the Tensorflow docs page for 
      the base `AdamOptimizer`_.
  
      .. _`MpiAdamOptimizer`: https://github.com/openai/baselines/blob/master/baselines/common/mpi_adam_optimizer.py
      .. _`AdamOptimizer`: https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer
      """
  
      def __init__(self, **kwargs):
          self.comm = MPI.COMM_WORLD
          tf.train.AdamOptimizer.__init__(self, **kwargs)
  
      def compute_gradients(self, loss, var_list, **kwargs):
          """
          Same as normal compute_gradients, except average grads over processes.
          """
          grads_and_vars = super().compute_gradients(loss, var_list, **kwargs)
          grads_and_vars = [(g, v) for g, v in grads_and_vars if g is not None]
          flat_grad = flat_concat([g for g, v in grads_and_vars])
          shapes = [v.shape.as_list() for g, v in grads_and_vars]
          sizes = [int(np.prod(s)) for s in shapes]
  
          num_tasks = self.comm.Get_size()
          buf = np.zeros(flat_grad.shape, np.float32)
  
          def _collect_grads(flat_grad):
              self.comm.Allreduce(flat_grad, buf, op=MPI.SUM)
              np.divide(buf, float(num_tasks), out=buf)
              return buf
  
          avg_flat_grad = tf.py_func(_collect_grads, [flat_grad], tf.float32)
          avg_flat_grad.set_shape(flat_grad.shape)
          avg_grads = tf.split(avg_flat_grad, sizes, axis=0)
          avg_grads_and_vars = [(tf.reshape(g, v.shape), v)
                      for g, (_, v) in zip(avg_grads, grads_and_vars)]
  
          return avg_grads_and_vars
  
      def apply_gradients(self, grads_and_vars, global_step=None, name=None):
          """
          Same as normal apply_gradients, except sync params after update.
          """
          opt = super().apply_gradients(grads_and_vars, global_step, name)
          with tf.control_dependencies([opt]):
              sync = sync_params([v for g,v in grads_and_vars])
          return tf.group([opt, sync])