code: def get_number_of_validation_epochs(
      training_epochs: int, evaluation_epochs: float
  ):
      """
      Given the number of training epochs and the number of epochs between model
      evaluations, return the number of times the model is being evaluated during
      training
  
      Args:
          training_epochs: The number of epochs to train for
          evaluation_epochs: This is the number of epochs after which we want to
              evaluate our model
  
      Returns:
          num_val_epochs: The number of epochs to be evaluated during training.
      """
      return training_epochs // evaluation_epochs
  
imports: import gc
  import logging
  import os
  import re
  import shutil
  from collections import OrderedDict
  from typing import Any, Dict
  import coolname
  import deepspeed
  import numpy
  import torch
  from deepspeed.runtime.dataloader import DeepSpeedDataLoader
  from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
  from peft import LoraConfig, PeftModel, get_peft_model
  from torch.cuda.amp import autocast
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from transformers import AutoConfig, AutoModel, BitsAndBytesConfig, GenerationMixin, StoppingCriteria, StoppingCriteriaList
  from transformers.pytorch_utils import Conv1D
  from transformers.utils import logging
  from llm_studio.src.datasets.text_utils import get_tokenizer
  from llm_studio.src.optimizers import Optimizers
  from llm_studio.src.schedulers import Schedulers
  from llm_studio.src.utils.config_utils import NON_GENERATION_PROBLEM_TYPES
  from llm_studio.src.utils.data_utils import OrderedDistributedSampler, batch_padding, cat_batches, get_inference_batch_size
  from llm_studio.src.utils.exceptions import LLMDataException, LLMModelException
  from llm_studio.src.utils.logging_utils import TqdmToLogger
  from llm_studio.src.utils.utils import save_pickle
label_desc: Use image node_id 12 for calling a global function with example usage: get_number_of_validation_epochs(training_epochs, evaluation_epochs) and returns: unknown