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","."]
docstr: Initializes the class.
  
  Args:
    remote_op_to_immediate_dep: A mapping from a remote op name to a list of
      remote op immediate dependencies' names.
docstr_tok: ["Initializes","the","class",".","Args",":","remote_op_to_immediate_dep",":","A","mapping","from","a","remote","op","name","to","a","list","of","remote","op","immediate","dependencies","'","names","."]
code: def __init__(
      self, remote_op_to_immediate_dep: Mapping[str, List[str]]
  ):
      """Initializes the class.
  
      Args:
        remote_op_to_immediate_dep: A mapping from a remote op name to a list of
          remote op immediate dependencies' names.
      """
      self.remote_op_to_immediate_dep = remote_op_to_immediate_dep
  
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 1 to create a new _RemoteOpLayers object  with example: obj = _RemoteOpLayers(remote_op_to_immediate_dep)