code: def train_cav(
      model_id,
      concepts: List[Concept],
      layers: Union[str, List[str]],
      classifier: Classifier,
      save_path: str,
      classifier_kwargs: Dict,
  ) -> Dict[str, Dict[str, CAV]]:
      r"""
      A helper function for parallel CAV computations that can be called
      from a python process.
  
      Please see the TCAV class documentation for further information.
  
      Args:
  
          model_id (str): A unique identifier for the PyTorch model for which
                  we would like to load the layer activations and train a
                  model in order to compute CAVs.
          concepts (list[Concept]): A list of Concept objects that are used
                  to train a classifier and learn decision boundaries between
                  those concepts for each layer defined in the `layers`
                  argument.
          layers (str or list[str]): A list of layer names or a single layer
                  name that is used to compute the activations of all concept
                  examples per concept and train a classifier using those
                  activations.
          classifier (Classifier): A custom classifier class, such as the
                  Sklearn "linear_model" that allows us to train a model
                  using the activation vectors extracted for a layer per concept.
                  It also allows us to access trained weights of the classifier
                  and the list of prediction classes.
          save_path (str): The path for storing Concept Activation
                  Vectors (CAVs) and Activation Vectors (AVs).
          classifier_kwargs (dict): Additional named arguments that are passed to
                  concept classifier's `train_and_eval` method.
  
      Returns:
          cavs (dict): A dictionary of CAV objects indexed by concept ids and
                  layer names. It gives access to the weights of each concept
                  in a given layer and model statistics such as accuracies
                  that resulted in trained concept weights.
      """
  
      concepts_key = concepts_to_str(concepts)
      cavs: Dict[str, Dict[str, CAV]] = defaultdict()
      cavs[concepts_key] = defaultdict()
      layers = [layers] if isinstance(layers, str) else layers
      for layer in layers:
          # Create data loader to initialize the trainer.
          datasets = [
              AV.load(save_path, model_id, concept.identifier, layer)
              for concept in concepts
          ]
  
          labels = [concept.id for concept in concepts]
  
          labelled_dataset = LabelledDataset(
              cast(List[AV.AVDataset], datasets), labels
          )
  
          def batch_collate(batch):
              inputs, labels = zip(*batch)
              return torch.cat(inputs), torch.cat(labels)
  
          dataloader = DataLoader(
              labelled_dataset, collate_fn=batch_collate
          )
  
          classifier_stats_dict = classifier.train_and_eval(
              dataloader, **classifier_kwargs
          )
          classifier_stats_dict = (
              {}
              if classifier_stats_dict is None
              else classifier_stats_dict
          )
  
          weights = classifier.weights()
          assert (
              weights is not None and len(weights) > 0
          ), "Model weights connot be None or empty"
  
          classes = classifier.classes()
          assert (
              classes is not None and len(classes) > 0
          ), "Classes cannot be None or empty"
  
          classes = (
              cast(torch.Tensor, classes).detach().numpy()
              if isinstance(classes, torch.Tensor)
              else classes
          )
          cavs[concepts_key][layer] = CAV(
              concepts,
              layer,
              {
                  "weights": weights,
                  "classes": classes,
                  **classifier_stats_dict,
              },
              save_path,
              model_id,
          )
          # Saving cavs on the disk
          cavs[concepts_key][layer].save()
  
      return cavs
  
imports: from collections import defaultdict
  from typing import Any, cast, Dict, List, Set, Tuple, Union
  import numpy
  import torch
  import torch.multiprocessing
  from captum._utils.av import AV
  from captum._utils.common import _format_tensor_into_tuples, _get_module_from_name
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr import LayerActivation, LayerAttribution, LayerGradientXActivation
  from captum.concept._core.cav import CAV
  from captum.concept._core.concept import Concept, ConceptInterpreter
  from captum.concept._utils.classifier import Classifier, DefaultClassifier
  from captum.concept._utils.common import concepts_to_str
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: train_cav(model_id, concepts, layers, classifier, save_path, classifier_kwargs) and returns: cavs, torch,  torch