parameters: (wt_init, num_wts, num_shards, name)
return_statement: return [tf.Variable(wt_init[i],
                          dtype=tf.float32,
                          name="%s_shard_%d" % (name, i))
              for i in xrange(num_shards)]
docstring: Helper functions to create sharded weight vector.
  
      Args:
        wt_init: init value for the weight. If None, weights are not created.
        num_wts: total size of all the weight shards
        num_shards: number of shards for the weights
        name: name for the new Variables.
  
      Returns:
        A list of weight shard Tensors.
docstring_summary: Helper functions to create sharded weight vector.
docstring_tokens: Helper functions to create sharded weight vector .
function: def _create_weights(wt_init, num_wts, num_shards, name):
      """Helper functions to create sharded weight vector.
  
      Args:
        wt_init: init value for the weight. If None, weights are not created.
        num_wts: total size of all the weight shards
        num_shards: number of shards for the weights
        name: name for the new Variables.
  
      Returns:
        A list of weight shard Tensors.
      """
      if wt_init is None:
        return None
      if num_shards == 1 and len(wt_init) == num_wts:
        wt_init = [wt_init]
      assert len(wt_init) == num_shards
      return [tf.Variable(wt_init[i],
                          dtype=tf.float32,
                          name="%s_shard_%d" % (name, i))
              for i in xrange(num_shards)]
function_tokens: def _create_weights ( wt_init , num_wts , num_shards , name ) : if wt_init is None : return None if num_shards == 1 and len ( wt_init ) == num_wts : wt_init = [ wt_init ] assert len ( wt_init ) == num_shards return [ tf . Variable ( wt_init [ i ] , dtype = tf . float32 , name = "%s_shard_%d" % ( name , i ) ) for i in xrange ( num_shards ) ]