code: def _flatten_params(_params: Tuple[Tensor, ...]) -> Tensor:
      """
      Given a tuple of tensors, which is how Pytorch represents parameters of a model,
      flattens it into a single tensor. This is useful if we want to do matrix operations
      on the parameters of a model, i.e. invert its Hessian, or compute dot-product of
      parameter-gradients. Note that flattening and then passing to standard linear
      algebra operations may not be the most efficient way to perform them.
      """
      return torch.cat([_param.view(-1) for _param in _params])
  
imports: import warnings
  from functools import reduce
  from typing import Any, Callable, Iterable, List, NamedTuple, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn
  from captum._utils.common import _get_module_from_name, _parse_version
  from captum._utils.gradient import _compute_jacobian_wrt_params, _compute_jacobian_wrt_params_with_sample_wise_trick
  from captum._utils.progress import progress
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 12 for calling a global function with example usage: _flatten_params(_params) and returns: torch