code: def test_keras_log_weights(
      dummy_model, dummy_data, relay_server, wandb_init
  ):
      with relay_server() as relay:
          run = wandb_init()
          dummy_model.fit(
              *dummy_data,
              epochs=2,
              batch_size=36,
              validation_data=dummy_data,
              callbacks=[
                  WandbCallback(
                      data_type="image",
                      log_weights=True,
                  ),
              ],
          )
          run.finish()
  
      history = relay.context.get_run_history(run.id)
      assert (
          history["parameters/dense.weights"][0]["_type"] == "histogram"
      )
  
imports: import glob
  import json
  import os
  import pytest
  import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras.layers import Dense, Flatten, Reshape
  from tensorflow.keras.models import Sequential
  from wandb.keras import WandbCallback
label_desc: Use image node_id 13 for calling a global function with example usage: test_keras_log_weights(dummy_model, dummy_data, relay_server, wandb_init) without return types