class_docstr: ignite handler to save metrics values and details into expected files.
  
  Args:
      save_dir: directory to save the metrics and metric details.
      metrics: expected final metrics to save into files, can be: None, "*" or list of strings.
          None - don't save any metrics into files.
          "*" - save all the existing metrics in `engine.state.metrics` dict into separate files.
          list of strings - specify the expected metrics to save.
          default to "*" to save all the metrics into `metrics.csv`.
      metric_details: expected metric details to save into files, the data comes from
          `engine.state.metric_details`, which should be provided by different `Metrics`,
          typically, it's some intermediate values in metric computation.
          for example: mean dice of every channel of every image in the validation dataset.
          it must contain at least 2 dims: (batch, classes, ...),
          if not, will unsqueeze to 2 dims.
          this arg can be: None, "*" or list of strings.
          None - don't save any metric_details into files.
          "*" - save all the existing metric_details in `engine.state.metric_details` dict into separate files.
          list of strings - specify the metric_details of expected metrics to save.
          if not None, every metric_details array will save a separate `{metric name}_raw.csv` file.
      batch_transform: a callable that is used to extract the `meta_data` dictionary of
          the input images from `ignite.engine.state.batch` if saving metric details. the purpose is to get the
          input filenames from the `meta_data` and store with metric details together.
          `engine.state` and `batch_transform` inherit from the ignite concept:
          https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
          https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
      summary_ops: expected computation operations to generate the summary report.
          it can be: None, "*" or list of strings, default to None.
          None - don't generate summary report for every expected metric_details.
          "*" - generate summary report for every metric_details with all the supported operations.
          list of strings - generate summary report for every metric_details with specified operations, they
          should be within list: ["mean", "median", "max", "min", "<int>percentile", "std", "notnans"].
          the number in "<int>percentile" should be [0, 100], like: "15percentile". default: "90percentile".
          for more details, please check: https://numpy.org/doc/stable/reference/generated/numpy.nanpercentile.html.
          note that: for the overall summary, it computes `nanmean` of all classes for each image first,
          then compute summary. example of the generated summary report::
  
              class    mean    median    max    5percentile 95percentile  notnans
              class0  6.0000   6.0000   7.0000   5.1000      6.9000       2.0000
              class1  6.0000   6.0000   6.0000   6.0000      6.0000       1.0000
              mean    6.2500   6.2500   7.0000   5.5750      6.9250       2.0000
  
      save_rank: only the handler on specified rank will save to files in multi-gpus validation, default to 0.
      delimiter: the delimiter character in the saved file, default to "," as the default output type is `csv`.
          to be consistent with: https://docs.python.org/3/library/csv.html#csv.Dialect.delimiter.
      output_type: expected output file type, supported types: ["csv"], default to "csv".
docstr: Initialize internal buffers.
  
  Args:
      _engine: Ignite Engine, unused argument.
docstr_tok: ["Initialize","internal","buffers",".","Args",":","_engine",":","Ignite","Engine",",","unused","argument","."]
code: def _started(self, _engine: Engine) -> None:
      """
      Initialize internal buffers.
  
      Args:
          _engine: Ignite Engine, unused argument.
  
      """
      self._filenames = []
  
file_path: MONAI/monai/handlers/metrics_saver.py
imports: from __future__ import annotations
  from collections.abc import Callable, Sequence
  from typing import TYPE_CHECKING
  from monai.config import IgniteInfo
  from monai.data import decollate_batch
  from monai.handlers.utils import write_metrics_reports
  from monai.utils import ImageMetaKey
  from monai.utils import ensure_tuple, min_version, optional_import, string_list_all_gather
label_desc: Use image node_id 3 for calling the MetricsSaver obj's underlying member method code with example usage: obj._started(_engine) without return types