docstr: Create artifacts for the input model and test batch.
  
  Args:
      model: Model instance being trained to use for inference.
      batch_dict: Test batch to generate artifacts for.
      initial_log: Flag to denote if this is the initial log, can
                   be used to save ground-truth data only once.
  
  Returns:
      List of audio and image artifacts to log.
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code: def generate_artifacts(
      self,
      model: LightningModule,
      batch_dict: Dict,
      initial_log: bool = False,
  ) -> Tuple[List[AudioArtifact], List[ImageArtifact]]:
      """
      Create artifacts for the input model and test batch.
  
      Args:
          model: Model instance being trained to use for inference.
          batch_dict: Test batch to generate artifacts for.
          initial_log: Flag to denote if this is the initial log, can
                       be used to save ground-truth data only once.
  
      Returns:
          List of audio and image artifacts to log.
      """
  
imports: import os
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple, Type
  import librosa
  import numpy
  import soundfile
  import torch
  from einops import rearrange
  from pytorch_lightning import Callback, LightningModule, Trainer
  from pytorch_lightning.loggers import TensorBoardLogger
  from pytorch_lightning.loggers.logger import Logger
  from pytorch_lightning.loggers.wandb import WandbLogger
  from torch import Tensor
  from nemo.collections.tts.parts.utils.helpers import create_plot
  from nemo.utils import logging
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 1 for calling the ArtifactGenerator obj's underlying member method code with example usage: obj.generate_artifacts(model, batch_dict, initial_log) without return types