code: def from_config(cls, file_path, **kwargs):
      with open(file_path, "r", encoding="utf-8") as f:
          cfg = yaml.load(f)
          batch_size = cfg["batch_size"]
          max_vocab_size_encoder = cfg["max_vocab_size_encoder"]
          max_vocab_size_decoder = cfg["max_vocab_size_decoder"]
          glove_path_encoder = cfg["embedding_path_encoder"]
          glove_path_decoder = cfg["embedding_path_decoder"]
          steps_per_epoch = cfg["steps_per_epoch"]
          tensorboard_dir = cfg["tensorboard_dir"]
          model_save_path = cfg["model_save_path"]
          use_bucketing = cfg["use_bucketing"]
          shuffle_buffer_size = cfg["shuffle_buffer_size"]
          bucketing_buffer_size_batches = cfg[
              "bucketing_buffer_size_batches"
          ]
          bucketing_batches_to_bucket = cfg[
              "bucketing_batches_to_bucket"
          ]
          steps_to_log = cfg["steps_to_log"]
          logging_level = logging.INFO
          logging_level_string = cfg["logging_level"]
          max_input_len = cfg["max_input_len"]
          max_output_len = cfg["max_output_len"]
          if logging_level_string == "debug":
              logging_level = logging.DEBUG
          elif logging_level_string == "error":
              logging_level = logging.ERROR
          return Trainer(
              batch_size=batch_size,
              max_vocab_size_encoder=max_vocab_size_encoder,
              max_vocab_size_decoder=max_vocab_size_decoder,
              embedding_path_encoder=glove_path_encoder,
              embedding_path_decoder=glove_path_decoder,
              steps_per_epoch=steps_per_epoch,
              tensorboard_dir=tensorboard_dir,
              model_save_path=model_save_path,
              use_bucketing=use_bucketing,
              shuffle_buffer_size=shuffle_buffer_size,
              bucketing_buffer_size_batches=bucketing_buffer_size_batches,
              bucketing_batches_to_bucket=bucketing_batches_to_bucket,
              logging_level=logging_level,
              steps_to_log=steps_to_log,
              max_input_len=max_input_len,
              max_output_len=max_output_len,
              **kwargs,
          )
  
imports: import datetime
  import logging
  import os
  import tempfile
  from collections import Counter
  from typing import Tuple, List, Iterable, Callable, Dict, Union
  import tensorflow
  import yaml
  from headliner.callbacks.evaluation_callback import EvaluationCallback
  from headliner.callbacks.model_checkpoint_callback import ModelCheckpointCallback
  from headliner.callbacks.tensorboard_callback import TensorboardCallback
  from headliner.callbacks.validation_callback import ValidationCallback
  from headliner.embeddings import read_embedding, embedding_to_matrix
  from headliner.evaluation.scorer import Scorer
  from headliner.losses import masked_crossentropy
  from headliner.model.bert_summarizer import BertSummarizer
  from headliner.model.summarizer import Summarizer
  from headliner.preprocessing.bucket_generator import BucketGenerator
  from headliner.preprocessing.dataset_generator import DatasetGenerator
  from headliner.preprocessing.keras_tokenizer import KerasTokenizer
  from headliner.preprocessing.preprocessor import Preprocessor
  from headliner.preprocessing.vectorizer import Vectorizer
  from headliner.utils.logger import get_logger
label_desc: Use image node_id 2 for calling the Trainer obj's underlying member method code with example usage: obj.from_config(cls, file_path) and returns: Trainer