code: def draw_kernel_feature_paths(
      model: GP, sample_shape: Size, **kwargs: Any
  ) -> GeneralizedLinearPath:
      r"""Draws functions from a Bayesian-linear-model-based approximation to a GP prior.
  
      When evaluted, sample paths produced by this method return Tensors with dimensions
      `sample_dims x batch_dims x [joint_dim]`, where `joint_dim` denotes the penultimate
      dimension of the input tensor. For multioutput models, outputs are returned as the
      final batch dimension.
  
      Args:
          model: The prior over functions.
          sample_shape: The shape of the sample paths to be drawn.
      """
      return DrawKernelFeaturePaths(
          model, sample_shape=sample_shape, **kwargs
      )
  
imports: from __future__ import annotations
  from typing import Any, Callable, List, Optional
  from botorch.models.approximate_gp import ApproximateGPyTorchModel
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.sampling.pathwise.features import gen_kernel_features
  from botorch.sampling.pathwise.features.generators import TKernelFeatureMapGenerator
  from botorch.sampling.pathwise.paths import GeneralizedLinearPath, PathList, SamplePath
  from botorch.sampling.pathwise.utils import get_input_transform, get_output_transform, get_train_inputs, TInputTransform, TOutputTransform
  from botorch.utils.dispatcher import Dispatcher
  from botorch.utils.sampling import draw_sobol_normal_samples
  from gpytorch.kernels import Kernel
  from gpytorch.models import ApproximateGP, ExactGP, GP
  from gpytorch.variational import _VariationalStrategy
  from torch import Size, Tensor
  from torch.nn import Module
label_desc: Use image node_id 1 for calling a global function with example usage: draw_kernel_feature_paths(model, sample_shape) and returns: DrawKernelFeaturePaths