docstr: Computes the gradients of the loss function with respect to the model's parameters for a single training and
  target pair.
  
  Parameters
  ----------
  model
      The model to compute gradients for.
  X
      The input data.
  Y
      The target data.
  loss_fn
      The loss function to use.
  
  Returns
  -------
  grads
      The gradients of the loss function with respect to the model's parameters. This is returned as a flattened             array.
docstr_tok: ["Computes","the","gradients","of","the","loss","function","with","respect","to","the","model","'s","parameters","for","a","single","training","and","target","pair",".","Parameters","--","--","--","--","--","model","The","model","to","compute","gradients","for",".","X","The","input","data",".","Y","The","target","data",".","loss_fn","The","loss","function","to","use",".","Returns","--","--","--","-","grads","The","gradients","of","the","loss","function","with","respect","to","the","model","'s","parameters",".","This","is","returned","as","a","flattened","array","."]
code: def get_grads(
      model: nn.Module,
      X: Union[torch.Tensor, List[Any]],
      Y: torch.Tensor,
      loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
  ) -> np.ndarray:
      """
          Computes the gradients of the loss function with respect to the model's parameters for a single training and
          target pair.
  
          Parameters
          ----------
          model
              The model to compute gradients for.
          X
              The input data.
          Y
              The target data.
          loss_fn
              The loss function to use.
  
          Returns
          -------
          grads
              The gradients of the loss function with respect to the model's parameters. This is returned as a flattened \
              array.
          """
  
      model.zero_grad()
      initial_model_state = model.training
      model.train(False)
      output = model(X)
      loss = loss_fn(output, Y)
      loss.backward()
      model.train(initial_model_state)
  
      return np.concatenate(
          [
              _PytorchBackend._grad_to_numpy(grad=param.grad, name=name)
              for name, param in model.named_parameters()
              if param.grad is not None
          ]
      )
  
file_path: alibi/alibi/explainers/similarity/backends/pytorch/base.py
imports: from typing import Any, Callable, List, Optional, Union
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 1 for calling the _PytorchBackend obj's underlying member method code with example usage: obj.get_grads(model, X, Y, loss_fn) and returns: np