class_docstr: This class finds the file path for the loaded image/label and writes the info
  into the data pipeline as a monai transforms.
  
  Args:
      key: the key to fetch the filename (for example, "image", "label").
      stats_name: the key to store the filename in the output stats report.
class_docstr_tok: ["This","class","finds","the","file","path","for","the","loaded","image\/label","and","writes","the","info","into","the","data","pipeline","as","a","monai","transforms",".","Args",":","key",":","the","key","to","fetch","the","filename","(","for","example",",","``","image","''",",","``","label","''",")",".","stats_name",":","the","key","to","store","the","filename","in","the","output","stats","report","."]
code: def __init__(self, key: str | None, stats_name: str) -> None:
      self.key = key
      super().__init__(stats_name, {})
  
imports: from __future__ import annotations
  import time
  from abc import ABC, abstractmethod
  from collections.abc import Hashable, Mapping
  from copy import deepcopy
  from typing import Any
  import numpy
  import torch
  from monai.apps.utils import get_logger
  from monai.auto3dseg.operations import Operations, SampleOperations, SummaryOperations
  from monai.auto3dseg.utils import concat_multikeys_to_dict, concat_val_to_np, get_foreground_image, get_foreground_label, get_label_ccp, verify_report_format
  from monai.bundle.config_parser import ConfigParser
  from monai.bundle.utils import ID_SEP_KEY
  from monai.data import MetaTensor, affine_to_spacing
  from monai.transforms.transform import MapTransform
  from monai.transforms.utils_pytorch_numpy_unification import sum, unique
  from monai.utils import convert_to_numpy
  from monai.utils.enums import DataStatsKeys, ImageStatsKeys, LabelStatsKeys
  from monai.utils.misc import ImageMetaKey, label_union
label_desc: Use image node_id 1 to create a new FilenameStats object from inherited base classes: Analyzer with example: obj = FilenameStats(key, stats_name)