docstr: Initializes model card with library versions and parameters.
docstr_tok: ["Initializes","model","card","with","library","versions","and","parameters","."]
code: def _initialize_model_card(self, **training_parameters):
      """Initializes model card with library versions and parameters."""
      # create a model card for this model with Flair and PyTorch version
      model_card = {
          "flair_version": flair.__version__,
          "pytorch_version": torch.__version__,
      }
  
      # record Transformers version if library is loaded
      try:
          import transformers
  
          model_card["transformers_version"] = transformers.__version__
      except ImportError:
          pass
  
      # remember all parameters used in train() call
      model_card["training_parameters"] = {
          k: str(v) if isinstance(v, Path) else v
          for k, v in training_parameters.items()
      }
  
      model_card["training_parameters"] = {
          k: f"{v.__module__}.{v.__name__}" if inspect.isclass(v) else v
          for k, v in training_parameters.items()
      }
  
      plugins = [
          plugin.get_state()
          for plugin in model_card["training_parameters"]["plugins"]
      ]
      model_card["training_parameters"]["plugins"] = plugins
  
      return model_card
  
imports: import contextlib
  import inspect
  import logging
  import os
  import random
  import time
  import warnings
  from inspect import signature
  from pathlib import Path
  from typing import List, Optional, Tuple, Type, Union
  import torch
  from torch.optim.sgd import SGD
  from torch.utils.data.dataset import ConcatDataset
  import flair
  import flair.nn
  from flair.data import Corpus, Dictionary, _len_dataset
  from flair.datasets import DataLoader
  from flair.samplers import FlairSampler
  from flair.trainers.plugins import AnnealingPlugin, CheckpointPlugin, LinearSchedulerPlugin, LogFilePlugin, LossFilePlugin, MetricName, MetricRecord, Pluggable, ReduceTransformerVocabPlugin, TrainerPlugin, TrainingInterrupt, WeightExtractorPlugin
  from flair.training_utils import identify_dynamic_embeddings, log_line, store_embeddings
label_desc: Use image node_id 14 for calling the ModelTrainer obj's underlying member method code with example usage: obj._initialize_model_card() and returns: model_card