code: def deep_kernel(request, backend, encoder_model):
      """
      Deep kernel, built using the `encoder_model` fixture for the projection, and using the kernel_a and eps
      parametrised in the test function.
      """
      # Get DeepKernel options
      kernel_a = request.param.get("kernel_a", "rbf")
      kernel_b = request.param.get("kernel_b", "rbf")
      eps = request.param.get("eps", "trainable")
  
      # Proj model (backend managed in encoder_model fixture)
      proj = encoder_model
  
      # Build DeepKernel
      if backend == "tensorflow":
          kernel_a = (
              GaussianRBF_tf(**kernel_a)
              if isinstance(kernel_a, dict)
              else kernel_a
          )
          kernel_b = (
              GaussianRBF_tf(**kernel_b)
              if isinstance(kernel_b, dict)
              else kernel_b
          )
          deep_kernel = DeepKernel_tf(
              proj, kernel_a=kernel_a, kernel_b=kernel_b, eps=eps
          )
      elif backend == "pytorch":
          kernel_a = (
              GaussianRBF_pt(**kernel_a)
              if isinstance(kernel_a, dict)
              else kernel_a
          )
          kernel_b = (
              GaussianRBF_pt(**kernel_b)
              if isinstance(kernel_b, dict)
              else kernel_b
          )
          deep_kernel = DeepKernel_pt(
              proj, kernel_a=kernel_a, kernel_b=kernel_b, eps=eps
          )
      elif backend == "keops":
          kernel_a = (
              GaussianRBF_ke(**kernel_a)
              if isinstance(kernel_a, dict)
              else kernel_a
          )
          kernel_b = (
              GaussianRBF_ke(**kernel_b)
              if isinstance(kernel_b, dict)
              else kernel_b
          )
          deep_kernel = DeepKernel_ke(
              proj, kernel_a=kernel_a, kernel_b=kernel_b, eps=eps
          )
      else:
          pytest.skip(
              "`deep_kernel` only implemented for tensorflow and pytorch."
          )
      return deep_kernel
  
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 6 for calling a global function with example usage: deep_kernel(request, backend, encoder_model) and returns: deep_kernel