code: def tf_model(n_hidden, input_size):
      import tensorflow as tf
  
      tf_model = tf.keras.Sequential(
          [
              tf.keras.layers.Dense(
                  n_hidden, input_shape=(input_size,)
              ),
              tf.keras.layers.LayerNormalization(),
              tf.keras.layers.Dense(n_hidden, activation="relu"),
              tf.keras.layers.LayerNormalization(),
              tf.keras.layers.Dense(10, activation="softmax"),
          ]
      )
      return tf_model
  
imports: import numpy
  import pytest
  from thinc.api import Adam, ArgsKwargs, Linear, Model, TensorFlowWrapper, get_current_ops, keras_subclass, tensorflow2xp, xp2tensorflow
  from thinc.compat import has_cupy_gpu, has_tensorflow
  from thinc.util import to_categorical
  from ..util import check_input_converters, make_tempdir
label_desc: Use image node_id 7 for calling a global function with example usage: tf_model(n_hidden, input_size) and returns: tf_model