code: def search_learning_rate(
      trainer: GradientDescentTrainer,
      start_lr: float = 1e-5,
      end_lr: float = 10,
      num_batches: int = 100,
      linear_steps: bool = False,
      stopping_factor: float = None,
  ) -> Tuple[List[float], List[float]]:
      """
      Runs training loop on the model using [`GradientDescentTrainer`](../training/trainer.md#gradientdescenttrainer)
      increasing learning rate from `start_lr` to `end_lr` recording the losses.
  
      # Parameters
  
      trainer: `GradientDescentTrainer`
      start_lr : `float`
          The learning rate to start the search.
      end_lr : `float`
          The learning rate upto which search is done.
      num_batches : `int`
          Number of batches to run the learning rate finder.
      linear_steps : `bool`
          Increase learning rate linearly if False exponentially.
      stopping_factor : `float`
          Stop the search when the current loss exceeds the best loss recorded by
          multiple of stopping factor. If `None` search proceeds till the `end_lr`
  
      # Returns
  
      (learning_rates, losses) : `Tuple[List[float], List[float]]`
          Returns list of learning rates and corresponding losses.
          Note: The losses are recorded before applying the corresponding learning rate
      """
      if num_batches <= 10:
          raise ConfigurationError(
              "The number of iterations for learning rate finder should be greater than 10."
          )
  
      trainer.model.train()
  
      infinite_generator = itertools.cycle(trainer.data_loader)
      train_generator_tqdm = Tqdm.tqdm(
          infinite_generator, total=num_batches
      )
  
      learning_rates = []
      losses = []
      best = 1e9
      if linear_steps:
          lr_update_factor = (end_lr - start_lr) / num_batches
      else:
          lr_update_factor = (end_lr / start_lr) ** (1.0 / num_batches)
  
      for i, batch in enumerate(train_generator_tqdm):
          if linear_steps:
              current_lr = start_lr + (lr_update_factor * i)
          else:
              current_lr = start_lr * (lr_update_factor**i)
  
          for param_group in trainer.optimizer.param_groups:
              param_group["lr"] = current_lr
              # Zero gradients.
              # NOTE: this is actually more efficient than calling `self.optimizer.zero_grad()`
              # because it avoids a read op when the gradients are first updated below.
              for p in param_group["params"]:
                  p.grad = None
  
          loss = trainer.batch_outputs(batch, for_training=True)["loss"]
          loss.backward()
          loss = loss.detach().cpu().item()
  
          if stopping_factor is not None and (
              math.isnan(loss) or loss > stopping_factor * best
          ):
              logger.info(
                  f"Loss ({loss}) exceeds stopping_factor * lowest recorded loss."
              )
              break
  
          trainer.rescale_gradients()
          trainer.optimizer.step()
  
          learning_rates.append(current_lr)
          losses.append(loss)
  
          if loss < best and i > 10:
              best = loss
  
          if i == num_batches:
              break
  
      return learning_rates, losses
  
imports: import argparse
  import logging
  import math
  import os
  import re
  from typing import List, Tuple
  import itertools
  from allennlp.commands.subcommand import Subcommand
  from allennlp.common import Params, Tqdm
  from allennlp.common import logging
  from allennlp.common.checks import check_for_gpu, ConfigurationError
  from allennlp.common.util import prepare_environment
  from allennlp.data import Vocabulary
  from allennlp.models import Model
  from allennlp.training import GradientDescentTrainer, Trainer
  from allennlp.training.util import create_serialization_dir, data_loaders_from_params
label_desc: Use image node_id 3 for calling a global function with example usage: search_learning_rate(trainer, start_lr, end_lr, num_batches, linear_steps, stopping_factor) and returns: learning_rates,  losses