code: def _get_execution_specs_for_remote_op_layer(
      remote_op_layer: Set[str],
      node_name_to_node_def: Mapping[str, tf.compat.v1.NodeDef],
  ) -> List[execution_spec.ExecutionSpec]:
      """Constructs ExecutionSpecs for a remote op layer.
  
      As discussed in _get_execution_specs(), a remote op layer contains one
      or more remote ops having no dependencies on each other. However, instead of
      having one ExecutionSpec to store a layer (as it is with subgraph layer),
      we use multiple ExecutionSpecs to represent a remote op layer.
  
      Args:
        remote_op_layer: A set of remote op names for a remote op layer.
        node_name_to_node_def: A mapping from node names to `NodeDef` protos.
  
      Returns:
        A list of ExecutionSpecs representing a remote op layer.
      """
      list_of_specs = []
  
      for remote_op_name in remote_op_layer:
          spec = execution_spec.ExecutionSpec(
              subgraph=None,
              input_names=set(
                  node_name_to_node_def[remote_op_name].input
              ),
              output_names=set([remote_op_name]),
              is_remote_op=True,
          )
          list_of_specs.append(spec)
  
      return list_of_specs
  
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 17 for calling a global function with example usage: _get_execution_specs_for_remote_op_layer(remote_op_layer, node_name_to_node_def) and returns: list_of_specs