class_docstr: A class that outputs remote op layers (custom topological sort).
  
  A remote op layer contains a set of remote op names that don't have
  dependencies on each other. The remote op layers are returned in execution
  order. In other words, a remote op layer returned earlier will be executed
  earlier.
class_docstr_tok: ["A","class","that","outputs","remote","op","layers","(","custom","topological","sort",")",".","A","remote","op","layer","contains","a","set","of","remote","op","names","that","do","n't","have","dependencies","on","each","other",".","The","remote","op","layers","are","returned","in","execution","order",".","In","other","words",",","a","remote","op","layer","returned","earlier","will","be","executed","earlier","."]
code: def __iter__(self):
      self._not_processed = set(self.remote_op_to_immediate_dep.keys())
      return self
  
imports: import collections
  from typing import Dict, List, Mapping, Set
  import tensorflow
  from tfx.dsl.io import fileio
  from tfx.experimental.distributed_inference.graphdef_experiments.subgraph_partitioning import execution_spec
label_desc: Use image node_id 2 for calling the _RemoteOpLayers obj's underlying member method code with example usage: obj.__iter__() and returns: self