code: def dev_collate(
      batch, level: int = 1, logger_name: str = "dev_collate"
  ):
      """
      Recursively run collate logic and provide detailed loggings for debugging purposes.
      It reports results at the 'critical' level, is therefore suitable in the context of exception handling.
  
      Args:
          batch: batch input to collate
          level: current level of recursion for logging purposes
          logger_name: name of logger to use for logging
  
      See also: https://pytorch.org/docs/stable/data.html#working-with-collate-fn
      """
      elem = batch[0]
      elem_type = type(elem)
      l_str = ">" * level
      batch_str = f"{batch[:10]}{' ... ' if len(batch) > 10 else ''}"
      if isinstance(elem, torch.Tensor):
          try:
              logging.getLogger(logger_name).critical(
                  f"{l_str} collate/stack a list of tensors"
              )
              return torch.stack(batch, 0)
          except TypeError as e:
              logging.getLogger(logger_name).critical(
                  f"{l_str} E: {e}, type {[type(elem).__name__ for elem in batch]} in collate({batch_str})"
              )
              return
          except RuntimeError as e:
              logging.getLogger(logger_name).critical(
                  f"{l_str} E: {e}, shape {[elem.shape for elem in batch]} in collate({batch_str})"
              )
              return
      elif (
          elem_type.__module__ == "numpy"
          and elem_type.__name__ != "str_"
          and elem_type.__name__ != "string_"
      ):
          if elem_type.__name__ in ["ndarray", "memmap"]:
              logging.getLogger(logger_name).critical(
                  f"{l_str} collate/stack a list of numpy arrays"
              )
              return dev_collate(
                  [torch.as_tensor(b) for b in batch],
                  level=level,
                  logger_name=logger_name,
              )
          elif elem.shape == ():  # scalars
              return batch
      elif isinstance(elem, (float, int, str, bytes)):
          return batch
      elif isinstance(elem, abc.Mapping):
          out = {}
          for key in elem:
              logging.getLogger(logger_name).critical(
                  f'{l_str} collate dict key "{key}" out of {len(elem)} keys'
              )
              out[key] = dev_collate(
                  [d[key] for d in batch],
                  level=level + 1,
                  logger_name=logger_name,
              )
          return out
      elif isinstance(elem, abc.Sequence):
          it = iter(batch)
          els = list(it)
          try:
              sizes = [len(elem) for elem in els]  # may not have `len`
          except TypeError:
              types = [type(elem).__name__ for elem in els]
              logging.getLogger(logger_name).critical(
                  f"{l_str} E: type {types} in collate({batch_str})"
              )
              return
          logging.getLogger(logger_name).critical(
              f"{l_str} collate list of sizes: {sizes}."
          )
          if any(s != sizes[0] for s in sizes):
              logging.getLogger(logger_name).critical(
                  f"{l_str} collate list inconsistent sizes, got size: {sizes}, in collate({batch_str})"
              )
          transposed = zip(*batch)
          return [
              dev_collate(
                  samples, level=level + 1, logger_name=logger_name
              )
              for samples in transposed
          ]
      logging.getLogger(logger_name).critical(
          f"{l_str} E: unsupported type in collate {batch_str}."
      )
      return
  
imports: from __future__ import annotations
  import hashlib
  import json
  import logging
  import math
  import os
  import pickle
  import sys
  from collections import abc, defaultdict
  from collections.abc import Generator, Iterable, Mapping, Sequence, Sized
  from copy import deepcopy
  from functools import reduce
  from itertools import product, starmap, zip_longest
  from pathlib import PurePath
  from typing import Any
  import numpy
  import torch
  from torch.utils.data._utils.collate import default_collate
  from monai import config
  from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor, PathLike
  from monai.data.meta_obj import MetaObj
  from monai.utils import MAX_SEED, BlendMode, Method, NumpyPadMode, TraceKeys, convert_data_type, convert_to_dst_type, ensure_tuple, ensure_tuple_rep, ensure_tuple_size, fall_back_tuple, first, get_equivalent_dtype, issequenceiterable, look_up_option, optional_import, pytorch_after
label_desc: Use image node_id 7 for calling a global function with example usage: dev_collate(batch, level, logger_name) and returns: torch, dev_collate, batch, batch, out, unknown