class_docstr: 🤗 Constructor for the image classifier trainer
code: def evaluate_f1_score(self):
      # Get the hypothesis and predictions
      all_target, all_preds = self.evaluate()
  
      table = metrics.classification_report(
          all_target,
          all_preds,
          labels=[int(a) for a in list(self.ids2labels.keys())],
          target_names=list(self.labels2ids.keys()),
          zero_division=0,
          digits=self.classification_report_digits,
      )
      print(table)
  
      # Write logs
      self.__openLogs()
      self.logs_file.write(table + "\n")
      self.logs_file.close()
  
      print("Logs saved at: \033[93m" + self.logs_path + "\033[0m")
      print(
          "\033[93m["
          + self.model_name
          + "]\033[0m Best model saved at: \033[93m"
          + self.best_path
          + "\033[0m"
          + " - Accuracy "
          + "{:.2f}".format(self.best_acc * 100)
      )
  
      return all_target, all_preds
  
imports: import os
  import json
  import math
  import random
  import argparse
  from pathlib import Path
  import multiprocessing
  from datetime import datetime
  from collections import Counter
  import torch
  import torchmetrics
  import torch.nn
  from torchmetrics import Accuracy
  import torchvision.models
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  from torch.optim.lr_scheduler import ExponentialLR
  from torch.utils.mobile_optimizer import optimize_for_mobile
  from sklearn import metrics
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import classification_report
  from tqdm import tqdm
  from tabulate import tabulate
  from PIL import Image, ImageEnhance
label_desc: Use image node_id 4 for calling the TorchVisionClassifierTrainer obj's underlying member method code with example usage: obj.evaluate_f1_score() and returns: all_target,  all_preds