code: def from_params(
      cls,
      model,
      serialization_dir,
      iterator,
      train_data,
      validation_data,
      params,
      validation_iterator=None,
  ):
      patience = params.pop_int("patience", None)
      validation_metric = params.pop("validation_metric", "-loss")
      shuffle = params.pop_bool("shuffle", True)
      num_epochs = params.pop_int("num_epochs", 20)
      cuda_device = params.pop_int("cuda_device", -1)
      grad_norm = params.pop_float("grad_norm", None)
      grad_clipping = params.pop_float("grad_clipping", None)
      lr_scheduler_params = params.pop("learning_rate_scheduler", None)
  
      if cuda_device >= 0:
          model = model.cuda(cuda_device)
      parameters = [
          [n, p] for n, p in model.named_parameters() if p.requires_grad
      ]
      optimizer = Optimizer.from_params(
          parameters, params.pop("optimizer")
      )
  
      if lr_scheduler_params:
          scheduler = LearningRateScheduler.from_params(
              optimizer, lr_scheduler_params
          )
      else:
          scheduler = None
  
      num_serialized_models_to_keep = params.pop_int(
          "num_serialized_models_to_keep", 20
      )
      keep_serialized_model_every_num_seconds = params.pop_int(
          "keep_serialized_model_every_num_seconds", None
      )
      model_save_interval = params.pop_float(
          "model_save_interval", None
      )
      summary_interval = params.pop_int("summary_interval", 100)
      histogram_interval = params.pop_int("histogram_interval", None)
  
      params.assert_empty(cls.__name__)
      return Trainer(
          model,
          optimizer,
          iterator,
          train_data,
          validation_data,
          patience=patience,
          validation_metric=validation_metric,
          validation_iterator=validation_iterator,
          shuffle=shuffle,
          num_epochs=num_epochs,
          serialization_dir=serialization_dir,
          cuda_device=cuda_device,
          grad_norm=grad_norm,
          grad_clipping=grad_clipping,
          learning_rate_scheduler=scheduler,
          num_serialized_models_to_keep=num_serialized_models_to_keep,
          keep_serialized_model_every_num_seconds=keep_serialized_model_every_num_seconds,
          model_save_interval=model_save_interval,
          summary_interval=summary_interval,
          histogram_interval=histogram_interval,
      )
  
imports: from __future__ import with_statement
  from __future__ import division
  from __future__ import absolute_import
  import logging
  import os
  import shutil
  import time
  import re
  import datetime
  import traceback
  import torch
  import torch.optim.lr_scheduler
  from torch.nn.parallel import replicate, parallel_apply
  from torch.nn.parallel.scatter_gather import scatter_kwargs, gather
  from allennlp.common import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.util import peak_memory_mb, gpu_memory_mb
  from allennlp.common.tqdm import Tqdm
  from allennlp.data.instance import Instance
  from allennlp.data.iterators.data_iterator import DataIterator
  from allennlp.models.model import Model
  from allennlp.nn import util
  from allennlp.training.learning_rate_schedulers import LearningRateScheduler
  from allennlp.training.optimizers import Optimizer
label_desc: Use image node_id 21 for calling the Trainer obj's underlying member method code with example usage: obj.from_params(cls, model, serialization_dir, iterator, train_data, validation_data, params, validation_iterator) and returns: Trainer