code: def _basic_computation_naive_influence_function(
      influence_inst: InfluenceFunctionBase,
      inputs: Tuple[Any, ...],
      targets: Optional[Tensor] = None,
      loss_fn: Optional[Union[Module, Callable]] = None,
      reduction_type: Optional[str] = None,
  ) -> Tensor:
      """
      This computes the per-example parameter gradients for a batch, flattened into a
      2D tensor where the first dimension is batch dimension. This is used by
      `NaiveInfluenceFunction` which computes embedding vectors for each example by
      projecting their parameter gradients.
      """
      # `jacobians` contains one tensor for each parameter we compute jacobians for.
      # the first dimension of each tensor is the batch dimension, and the remaining
      # dimensions correspond to the parameter, so that for the tensor corresponding
      # to parameter `p`, its shape is `(batch_size, *p.shape)`
      jacobians = _compute_jacobian_sample_wise_grads_per_batch(
          influence_inst, inputs, targets, loss_fn, reduction_type
      )
  
      return torch.stack(
          [
              _flatten_params(
                  tuple(jacobian[i] for jacobian in jacobians)
              )
              for i in range(len(next(iter(jacobians))))
          ],
          dim=0,
      )
  
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 4 for calling a global function with example usage: _basic_computation_naive_influence_function(influence_inst, inputs, targets, loss_fn, reduction_type) and returns: torch