code: def classifier_model(backend, current_cases):
      """
      Classification model with given input dimension and backend.
      """
      _, _, data_params = current_cases["data"]
      _, input_dim = data_params["data_shape"]
      if backend == "tensorflow":
          model = tf.keras.Sequential(
              [
                  tf.keras.layers.InputLayer(input_shape=(input_dim,)),
                  tf.keras.layers.Dense(2, activation=tf.nn.softmax),
              ]
          )
      elif backend in ("pytorch", "keops"):
          model = nn.Sequential(nn.Linear(input_dim, 2), nn.Softmax(1))
      elif backend == "sklearn":
          model = RandomForestClassifier()
      else:
          pytest.skip(
              "`classifier_model` only implemented for tensorflow, pytorch, keops and sklearn."
          )
      return model
  
imports: from functools import partial
  from importlib import import_module
  import numpy
  import tensorflow
  import torch
  import torch.nn
  from sklearn.ensemble import RandomForestClassifier
  from xgboost import XGBClassifier
  from requests.exceptions import HTTPError
  import pytest
  from pytest_cases import fixture, parametrize
  from transformers import AutoTokenizer
  from alibi_detect.cd.pytorch import UAE
  from alibi_detect.cd.pytorch import preprocess_drift
  from alibi_detect.cd.tensorflow import UAE
  from alibi_detect.cd.tensorflow import preprocess_drift
  from alibi_detect.utils.pytorch.kernels import GaussianRBF
  from alibi_detect.utils.pytorch.kernels import DeepKernel
  from alibi_detect.utils.tensorflow.kernels import GaussianRBF
  from alibi_detect.utils.tensorflow.kernels import DeepKernel
  from alibi_detect.models.pytorch import TransformerEmbedding
  from alibi_detect.models.tensorflow import TransformerEmbedding
  from alibi_detect.cd.pytorch import HiddenOutput
  from alibi_detect.cd.tensorflow import HiddenOutput
  from alibi_detect.utils.frameworks import has_keops
label_desc: Use image node_id 7 for calling a global function with example usage: classifier_model(backend, current_cases) and returns: model