code: def dummy_model(request):
      K.clear_session()
      multi = request.node.get_closest_marker("multiclass")
      image_output = request.node.get_closest_marker("image_output")
      loss, nodes = (
          ("categorical_crossentropy", 10)
          if multi
          else ("binary_crossentropy", 1)
      )
      if image_output:
          nodes = 300
      model = Sequential()
      model.add(Flatten(input_shape=(10, 10, 3)))
      model.add(Dense(nodes, activation="sigmoid"))
      if image_output:
          model.add(Dense(nodes, activation="relu"))
          model.add(Reshape((10, 10, 3)))
      model.compile(optimizer="adam", loss=loss, metrics=["accuracy"])
      return model
  
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 1 for calling a global function with example usage: dummy_model(request) and returns: model