parameters: (self,
                                    debug_dump,
                                    tf_error,
                                    passed_filter,
                                    passed_filter_exclude_node_names)
docstring: Prepare (but not launch) CLI for run-end, with debug dump from the run.
  
      Args:
        debug_dump: (debug_data.DebugDumpDir) The debug dump directory from this
          run.
        tf_error: (None or OpError) OpError that happened during the run() call
          (if any).
        passed_filter: (None or str) Name of the tensor filter that just passed
          and caused the preparation of this run-end CLI (if any).
        passed_filter_exclude_node_names: (None or str) Regular expression used
          with the tensor filter to exclude ops with names matching the regular
          expresssion.
docstring_summary: Prepare (but not launch) CLI for run-end, with debug dump from the run.
docstring_tokens: Prepare ( but not launch ) CLI for run - end with debug dump from the run .
function: def _prep_debug_cli_for_run_end(self,
                                    debug_dump,
                                    tf_error,
                                    passed_filter,
                                    passed_filter_exclude_node_names):
      """Prepare (but not launch) CLI for run-end, with debug dump from the run.
  
      Args:
        debug_dump: (debug_data.DebugDumpDir) The debug dump directory from this
          run.
        tf_error: (None or OpError) OpError that happened during the run() call
          (if any).
        passed_filter: (None or str) Name of the tensor filter that just passed
          and caused the preparation of this run-end CLI (if any).
        passed_filter_exclude_node_names: (None or str) Regular expression used
          with the tensor filter to exclude ops with names matching the regular
          expresssion.
      """
  
      if tf_error:
        help_intro = cli_shared.get_error_intro(tf_error)
  
        self._init_command = "help"
        self._title_color = "red_on_white"
      else:
        help_intro = None
        self._init_command = "lt"
  
        self._title_color = "black_on_white"
        if passed_filter is not None:
          # Some dumped tensor(s) from this run passed the filter.
          self._init_command = "lt -f %s" % passed_filter
          if passed_filter_exclude_node_names:
            self._init_command += (" --filter_exclude_node_names %s" %
                                   passed_filter_exclude_node_names)
          self._title_color = "red_on_white"
  
      self._run_cli = analyzer_cli.create_analyzer_ui(
          debug_dump,
          self._tensor_filters,
          ui_type=self._ui_type,
          on_ui_exit=self._remove_dump_root,
          config=self._config)
  
      # Get names of all dumped tensors.
      dumped_tensor_names = []
      for datum in debug_dump.dumped_tensor_data:
        dumped_tensor_names.append("%s:%d" %
                                   (datum.node_name, datum.output_slot))
  
      # Tab completions for command "print_tensors".
      self._run_cli.register_tab_comp_context(["print_tensor", "pt"],
                                              dumped_tensor_names)
  
      # Tab completion for commands "node_info", "list_inputs" and
      # "list_outputs". The list comprehension is used below because nodes()
      # output can be unicodes and they need to be converted to strs.
      self._run_cli.register_tab_comp_context(
          ["node_info", "ni", "list_inputs", "li", "list_outputs", "lo"],
          [str(node_name) for node_name in debug_dump.nodes()])
      # TODO(cais): Reduce API surface area for aliases vis-a-vis tab
      #    completion contexts and registered command handlers.
  
      self._title = "run-end: " + self._run_description
  
      if help_intro:
        self._run_cli.set_help_intro(help_intro)
function_tokens: def _prep_debug_cli_for_run_end ( self , debug_dump , tf_error , passed_filter , passed_filter_exclude_node_names ) : if tf_error : help_intro = cli_shared . get_error_intro ( tf_error ) self . _init_command = "help" self . _title_color = "red_on_white" else : help_intro = None self . _init_command = "lt" self . _title_color = "black_on_white" if passed_filter is not None : # Some dumped tensor(s) from this run passed the filter. self . _init_command = "lt -f %s" % passed_filter if passed_filter_exclude_node_names : self . _init_command += ( " --filter_exclude_node_names %s" % passed_filter_exclude_node_names ) self . _title_color = "red_on_white" self . _run_cli = analyzer_cli . create_analyzer_ui ( debug_dump , self . _tensor_filters , ui_type = self . _ui_type , on_ui_exit = self . _remove_dump_root , config = self . _config ) # Get names of all dumped tensors. dumped_tensor_names = [ ] for datum in debug_dump . dumped_tensor_data : dumped_tensor_names . append ( "%s:%d" % ( datum . node_name , datum . output_slot ) ) # Tab completions for command "print_tensors". self . _run_cli . register_tab_comp_context ( [ "print_tensor" , "pt" ] , dumped_tensor_names ) # Tab completion for commands "node_info", "list_inputs" and # "list_outputs". The list comprehension is used below because nodes() # output can be unicodes and they need to be converted to strs. self . _run_cli . register_tab_comp_context ( [ "node_info" , "ni" , "list_inputs" , "li" , "list_outputs" , "lo" ] , [ str ( node_name ) for node_name in debug_dump . nodes ( ) ] ) # TODO(cais): Reduce API surface area for aliases vis-a-vis tab #    completion contexts and registered command handlers. self . _title = "run-end: " + self . _run_description if help_intro : self . _run_cli . set_help_intro ( help_intro )