code: def _layer_trace(
      traces: NamedTuple,
      trace_events: Any,
      show_events: bool = True,
      paths: List[str] = None,
      use_cuda: bool = False,
      profile_memory: bool = False,
      dt: Tuple[str, ...] = ("-", "-", "-", " "),
  ) -> object:
      """Construct human readable output of the profiler traces and events. The
      information is presented in layers, and each layer contains its underlying
      operators.
  
      Args:
          traces (trace object): Raw trace to be parsed.
          trace_events (trace object): Raw events to be parsed.
          show_events (bool, optional): If True, show detailed event information.
              (default: :obj:`True`)
          paths (str, optional): Predefine path for fast loading. By default, it
              will not be used.
              (default: :obj:`False`)
          use_cuda (bool, optional): Enables timing of CUDA events.
              (default: :obj:`False`)
          profile_memory (bool, optional): If True, also profile for the memory
              usage information.
              (default: :obj:`False`)
          dt (object, optional): Delimiters for showing the events.
      """
      tree = OrderedDict()
  
      for trace in traces:
          [path, leaf, module] = trace
          current_tree = tree
          # unwrap all of the events, in case model is called multiple times
          events = [
              te for t_events in trace_events[path] for te in t_events
          ]
          for depth, name in enumerate(path, 1):
              if name not in current_tree:
                  current_tree[name] = OrderedDict()
              if depth == len(path) and (
                  (paths is None and leaf)
                  or (paths is not None and path in paths)
              ):
                  # tree measurements have key None, avoiding name conflict
                  if show_events:
                      for event_name, event_group in _group_by(
                          events, lambda e: e.name
                      ):
                          event_group = list(event_group)
                          current_tree[name][event_name] = {
                              None: _build_measure_tuple(
                                  event_group, len(event_group)
                              )
                          }
                  else:
                      current_tree[name][None] = _build_measure_tuple(
                          events, len(trace_events[path])
                      )
              current_tree = current_tree[name]
      tree_lines = _flatten_tree(tree)
  
      format_lines = []
      has_self_cuda_total = False
      has_self_cpu_memory = False
      has_cpu_memory = False
      has_self_cuda_memory = False
      has_cuda_memory = False
  
      raw_results = {}
      for idx, tree_line in enumerate(tree_lines):
          depth, name, measures = tree_line
  
          next_depths = [pl[0] for pl in tree_lines[idx + 1 :]]
          pre = "-"
          if depth > 0:
              pre = (
                  dt[1]
                  if depth in next_depths and next_depths[0] >= depth
                  else dt[2]
              )
              depth -= 1
          while depth > 0:
              pre = (
                  (dt[0] + pre)
                  if depth in next_depths
                  else (dt[3] + pre)
              )
              depth -= 1
  
          format_lines.append(
              [pre + name, *_format_measure_tuple(measures)]
          )
          if measures:
              has_self_cuda_total = (
                  has_self_cuda_total
                  or measures.self_cuda_total is not None
              )
              has_self_cpu_memory = (
                  has_self_cpu_memory
                  or measures.self_cpu_memory is not None
              )
              has_cpu_memory = (
                  has_cpu_memory or measures.cpu_memory is not None
              )
              has_self_cuda_memory = (
                  has_self_cuda_memory
                  or measures.self_cuda_memory is not None
              )
              has_cuda_memory = (
                  has_cuda_memory or measures.cuda_memory is not None
              )
  
              raw_results[name] = [
                  measures.self_cpu_total,
                  measures.cpu_total,
                  measures.self_cuda_total,
                  measures.cuda_total,
                  measures.self_cpu_memory,
                  measures.cpu_memory,
                  measures.self_cuda_memory,
                  measures.cuda_memory,
                  measures.occurrences,
              ]
  
      # construct the table (this is pretty ugly and can probably be optimized)
      heading = (
          "Module",
          "Self CPU total",
          "CPU total",
          "Self CUDA total",
          "CUDA total",
          "Self CPU Mem",
          "CPU Mem",
          "Self CUDA Mem",
          "CUDA Mem",
          "Number of Calls",
      )
  
      # get the output aligned
      max_lens = [
          max(map(len, col)) for col in zip(*([heading] + format_lines))
      ]
  
      # not all columns should be displayed, specify kept indexes
      keep_indexes = [0, 1, 2, 9]
      if profile_memory:
          if has_self_cpu_memory:
              keep_indexes.append(5)
          if has_cpu_memory:
              keep_indexes.append(6)
      if use_cuda:
          if has_self_cuda_total:
              keep_indexes.append(3)
          keep_indexes.append(4)
          if profile_memory:
              if has_self_cuda_memory:
                  keep_indexes.append(7)
              if has_cuda_memory:
                  keep_indexes.append(8)
  
      # the final columns to be shown
      keep_indexes = tuple(sorted(keep_indexes))
  
      heading_list = list(heading)
  
      display = (  # table heading
          " | ".join(
              [
                  "{:<{}s}".format(
                      heading[keep_index], max_lens[keep_index]
                  )
                  for keep_index in keep_indexes
              ]
          )
          + "\n"
      )
      display += (  # separator
          "-|-".join(
              [
                  "-" * max_len
                  for val_idx, max_len in enumerate(max_lens)
                  if val_idx in keep_indexes
              ]
          )
          + "\n"
      )
      for format_line in format_lines:  # body
          display += (
              " | ".join(
                  [
                      "{:<{}s}".format(value, max_lens[val_idx])
                      for val_idx, value in enumerate(format_line)
                      if val_idx in keep_indexes
                  ]
              )
              + "\n"
          )
      # layer information readable
      key_dict = {}
      layer_names = []
      layer_stats = []
      for format_line in format_lines:  # body
          if format_line[1] == "":  # key line
              key_dict[format_line[0].count("-")] = format_line[0]
          else:  # must print
              # get current line's level
              curr_level = format_line[0].count("-")
              par_str = ""
              for i in range(1, curr_level):
                  par_str += key_dict[i]
              curr_key = par_str + format_line[0]
              layer_names.append(curr_key)
              layer_stats.append(format_line[1:])
  
      return (
          display,
          heading_list,
          raw_results,
          layer_names,
          layer_stats,
      )
  
imports: import functools
  from collections import OrderedDict, defaultdict, namedtuple
  from typing import Any, List, NamedTuple, Optional, Tuple
  import torch
  import torch.profiler
label_desc: Use image node_id 1 for calling a global function with example usage: _layer_trace(traces, trace_events, show_events, paths, use_cuda, profile_memory, dt) and returns: display,  heading_list,  raw_results,  layer_names,  layer_stats