docstr: Set the lr_finder_args.
  This is the range of learning rates to test.
code: def set_lr_finder_args(self, dataset_size, num_batches):
      """
      Set the lr_finder_args.
      This is the range of learning rates to test.
      """
      num_training = 150 + int(np.log10(100 + dataset_size) * 25)
      if num_batches < num_training:
          log.warning(
              f"Learning rate finder: The number of batches ({num_batches}) is too small than the required number \
                      for the learning rate finder ({num_training}). The results might not be optimal."
          )
          # num_training = num_batches
      self.lr_finder_args.update(
          {
              "min_lr": 1e-6,
              "max_lr": 10,
              "num_training": num_training,
              "early_stop_threshold": None,
          }
      )
  
imports: from __future__ import annotations
  import logging
  import math
  import types
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from typing import Callable, List, Optional
  from typing import OrderedDict
  from typing import Type, Union
  import numpy
  import pandas
  import torch
  from neuralprophet import df_utils, np_types, utils, utils_torch
  from neuralprophet.custom_loss_metrics import PinballLoss
label_desc: Use image node_id 7 for calling the Train obj's underlying member method code with example usage: obj.set_lr_finder_args(dataset_size, num_batches) without return types