class_docstr: The ModelGraph represents the network that is being processed by hls4ml.
  
  Args:
      config (dict):  The configuration dictionary
      layer_list (list(dict)):  The list contains a dictionary for each input layer
      inputs (list, optional):  The inputs to the model. If None, determined from layer_list
      outputs (list, optional):  The outputs to the model. If None, determined from layer_list
docstr: Given a list of all layers, and a list input/output names, find the names of their outputs that will be used
  as the name of the output variables.
docstr_tok: ["Given","a","list","of","all","layers",",","and","a","list","input\/output","names",",","find","the","names","of","their","outputs","that","will","be","used","as","the","name","of","the","output","variables","."]
code: def _find_output_variable_names(self, layer_list, layer_names):
      """Given a list of all layers, and a list input/output names, find the names of their outputs that will be used
      as the name of the output variables."""
      inout_nodes = []
      for layer_name in layer_names:
          for node in layer_list:
              if node["name"] == layer_name:
                  inout_nodes.append(node)
      all_node_output_names = [
          node["outputs"] if "outputs" in node else [node["name"]]
          for node in inout_nodes
      ]
      return [
          output
          for node_output_names in all_node_output_names
          for output in node_output_names
      ]
  
imports: import ctypes
  import os
  import platform
  from collections import OrderedDict
  import numpy
  import numpy.ctypeslib
  from hls4ml.backends import get_backend
  from hls4ml.model.flow import get_flow
  from hls4ml.model.layers import layer_map
  from hls4ml.model.optimizer import get_available_passes, optimize_model
label_desc: Use image node_id 2 for calling the ModelGraph obj's underlying member method code with example usage: obj._find_output_variable_names(layer_list, layer_names) and returns: unknown