class_docstr: Args:
      sortish_sampler (`bool`, *optional*, defaults to `False`):
          Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset*
          for now but will become generally available in the near future.
  
          It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness
          for the training set.
      predict_with_generate (`bool`, *optional*, defaults to `False`):
          Whether to use generate to calculate generative metrics (ROUGE, BLEU).
      generation_max_length (`int`, *optional*):
          The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
          `max_length` value of the model configuration.
      generation_num_beams (`int`, *optional*):
          The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
          `num_beams` value of the model configuration.
      generation_config (`str` or `Path` or [`~generation.GenerationConfig`], *optional*):
          Allows to load a [`~generation.GenerationConfig`] from the `from_pretrained` method. This can be either:
  
          - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
            huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
            under a user or organization name, like `dbmdz/bert-base-german-cased`.
          - a path to a *directory* containing a configuration file saved using the
            [`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
          - a [`~generation.GenerationConfig`] object.
docstr: Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
  serialization support). It obfuscates the token values by removing their value.
code: def to_dict(self):
      """
      Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
      serialization support). It obfuscates the token values by removing their value.
      """
      # filter out fields that are defined as field(init=False)
      d = super().to_dict()
      for k, v in d.items():
          if isinstance(v, GenerationConfig):
              d[k] = v.to_dict()
      return d
  
file_path: transformers/src/transformers/training_args_seq2seq.py
imports: import logging
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Optional, Union
  from .generation.configuration_utils import GenerationConfig
  from .training_args import TrainingArguments
  from .utils import add_start_docstrings
label_desc: Use image node_id 1 for calling the Seq2SeqTrainingArguments obj's underlying member method code with example usage: obj.to_dict() and returns: d