code: def _flatten_forward_factory(
      model: nn.Module,
      loss_fn: Optional[Union[Module, Callable]],
      reduction_type: str,
      unflatten_fn: Callable,
      param_names: List[str],
  ):
      """
      Given a model, loss function, reduction type of the loss, function that unflattens
      1D tensor input into a tuple of tensors, the name of each tensor in that tuple,
      each of which represents a parameter of `model`, and returns a factory. The factory
      accepts a batch, and returns a function whose input is the parameters represented
      by `param_names`, and output is the total loss of the model with those parameters,
      calculated on the batch. The parameter input to the returned function is assumed to
      be *flattened* via the inverse of `unflatten_fn`, which takes a tuple of tensors to
      a 1D tensor. This returned function, accepting a single flattened 1D parameter, is
      useful for computing the parameter gradient involving the batch as a 1D tensor, and
      the Hessian involving the batch as a 2D tensor. Both quantities are needed to
      calculate the kind of influence scores returned by implementations of
      `InfluenceFunctionBase`.
      """
  
      # this is the factory that accepts a batch
      def flatten_forward_factory_given_batch(batch):
          # this is the function that factory returns, which is a function of flattened
          # parameters
          def flattened_forward(flattened_params):
              # as everywhere else, the all but the last elements of a batch are
              # assumed to correspond to the features, i.e. input to forward function
              features, labels = tuple(batch[0:-1]), batch[-1]
  
              _output = _functional_call(
                  model,
                  dict(
                      zip(param_names, unflatten_fn(flattened_params))
                  ),
                  features,
              )
  
              # compute the total loss for the batch, adjusting the output of
              # `loss_fn` based on `reduction_type`
              return _compute_batch_loss_influence_function_base(
                  loss_fn, _output, labels, reduction_type
              )
  
          return flattened_forward
  
      return flatten_forward_factory_given_batch
  
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 a global function with example usage: _flatten_forward_factory(model, loss_fn, reduction_type, unflatten_fn, param_names) and returns: flatten_forward_factory_given_batch, flattened_forward, _compute_batch_loss_influence_function_base