class_docstr: Implementations of this class all implement the `compute_intermediate_quantities`
  method, which computes the "embedding" vectors for all examples in a test dataset.
  These embedding vectors are assumed to have the following properties:
  - the influence score of one example on another example, as calculated by the
    implementation, is the dot-product of their respective embeddings.
  - the self influence score of an example is the squared norm of its embedding.
code: def compute_intermediate_quantities(
      self,
      inputs_dataset: Union[Tuple[Any, ...], DataLoader],
      aggregate: bool = False,
      show_progress: bool = False,
      return_on_cpu: bool = True,
      test: bool = False,
  ):
      pass
  
imports: import functools
  from abc import abstractmethod
  from operator import add
  from typing import Any, Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from captum._utils.gradient import _extract_parameters_from_layers
  from captum.influence._core.influence import DataInfluence
  from captum.influence._utils.common import _check_loss_fn, _compute_batch_loss_influence_function_base, _compute_jacobian_sample_wise_grads_per_batch, _dataset_fn, _flatten_params, _format_inputs_dataset, _functional_call, _get_k_most_influential_helper, _influence_batch_intermediate_quantities_influence_function, _influence_helper_intermediate_quantities_influence_function, _influence_route_to_helpers, _load_flexible_state_dict, _params_to_names, _progress_bar_constructor, _self_influence_helper_intermediate_quantities_influence_function, _set_active_parameters, _top_eigen, _unflatten_params_factory, KMostInfluentialResults
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader, Dataset
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling the IntermediateQuantitiesInfluenceFunction obj's underlying member method code with example usage: obj.compute_intermediate_quantities(inputs_dataset, aggregate, show_progress, return_on_cpu, test) without return types