code: def tf_linear_model(input_shape, output_shape):
      """
      Constructs a linear model for `tensorflow`.
      """
      return keras.Sequential(
          [
              tf.keras.layers.InputLayer(input_shape=input_shape),
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(output_shape),
          ]
      )
  
imports: import pytest
  import random
  import os
  import numpy
  from tensorflow import keras
  import tensorflow
  import torch
  import torch.nn
  from sklearn.datasets import make_classification, make_regression
  from sklearn.model_selection import train_test_split
  from alibi.explainers.similarity.backends.pytorch.base import _PytorchBackend
  from alibi.explainers.similarity.backends.tensorflow.base import _TensorFlowBackend
label_desc: Use image node_id 9 for calling a global function with example usage: tf_linear_model(input_shape, output_shape) and returns: keras