code: def linear_cls_model(request):
      """
      Constructs a linear classification model, loss function, and target function.
      """
      input_shape = request.param.get("input_shape", (10,))
      output_shape = request.param.get("output_shape", 10)
      framework = request.param.get("framework", "tensorflow")
  
      model = {
          "tensorflow": lambda i_shape, o_shape: tf_linear_model(
              i_shape, o_shape
          ),
          "pytorch": lambda i_shape, o_shape: torch_linear_model(
              i_shape, o_shape
          ),
      }[framework](input_shape, output_shape)
  
      loss_fn = {
          "tensorflow": tf.keras.losses.SparseCategoricalCrossentropy,
          "pytorch": nn.CrossEntropyLoss,
      }[framework]()
  
      target_fn = {
          "tensorflow": lambda x: np.argmax(model(x), axis=1),
          "pytorch": lambda x: torch.argmax(model(x), dim=1),
      }[framework]
  
      return framework, model, loss_fn, target_fn
  
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 6 for calling a global function with example usage: linear_cls_model(request) and returns: framework,  model,  loss_fn,  target_fn