code: def test_tensorboard_graph_log(
      dataloaders_with_covariates, model, tmp_path
  ):
      d = next(iter(dataloaders_with_covariates["train"]))
      logger = TensorBoardLogger("test", str(tmp_path), log_graph=True)
      logger.log_graph(model, d[0])
  
imports: import pickle
  import shutil
  import sys
  import lightning.pytorch
  from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
  from lightning.pytorch.loggers import TensorBoardLogger
  import numpy
  import pytest
  import torch
  from pytorch_forecasting import TimeSeriesDataSet
  from pytorch_forecasting.data import NaNLabelEncoder
  from pytorch_forecasting.data.encoders import GroupNormalizer, MultiNormalizer
  from pytorch_forecasting.metrics import CrossEntropy, MQF2DistributionLoss, MultiLoss, NegativeBinomialDistributionLoss, PoissonLoss, QuantileLoss, TweedieLoss
  from pytorch_forecasting.models import TemporalFusionTransformer
  from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
  from test_models.conftest import make_dataloaders
label_desc: Use image node_id 7 for calling a global function with example usage: test_tensorboard_graph_log(dataloaders_with_covariates, model, tmp_path) without return types