code: def set_auto_batch_epoch(
      self,
      n_data: int,
      min_batch: int = 32,
      max_batch: int = 1024,
      min_epoch: int = 10,
      max_epoch: int = 1000,
  ):
      assert n_data >= 1
      self.n_data = n_data
      if self.batch_size is None:
          self.batch_size = int(2 ** (3 + int(np.log10(n_data))))
          self.batch_size = min(
              max_batch, max(min_batch, self.batch_size)
          )
          self.batch_size = min(self.n_data, self.batch_size)
          log.info(f"Auto-set batch_size to {self.batch_size}")
      if self.epochs is None:
          # this should (with auto batch size) yield about 1000 steps minimum and 100,000 steps at upper cutoff
          self.epochs = int(
              2 ** (2.5 * np.log10(100 + n_data)) / (n_data / 1000.0)
          )
          self.epochs = min(max_epoch, max(min_epoch, self.epochs))
          log.info(f"Auto-set epochs to {self.epochs}")
      # also set lambda_delay:
      self.lambda_delay = int(self.reg_delay_pct * self.epochs)
  
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 4 for calling the Train obj's underlying member method code with example usage: obj.set_auto_batch_epoch(n_data, min_batch, max_batch, min_epoch, max_epoch) without return types