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 point.
  Y
      The target data point.
  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","point",".","Y","The","target","data","point",".","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: keras.Model,
      X: Union[tf.Tensor, List[Any]],
      Y: tf.Tensor,
      loss_fn: Callable[[tf.Tensor, tf.Tensor], tf.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 point.
          Y
              The target data point.
          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.
          """
  
      with tf.device(_TensorFlowBackend.device):
          with tf.GradientTape() as tape:
              output = model(X, training=False)
              loss = loss_fn(Y, output)
  
          # compute gradients of the loss w.r.t the weights
          grad_X_train = tape.gradient(loss, model.trainable_weights)
          grad_X_train = np.concatenate(
              [
                  _TensorFlowBackend._grad_to_numpy(
                      w, getattr(w, "name", None)
                  )
                  for w in grad_X_train
              ]
          )
      return grad_X_train
  
file_path: alibi/alibi/explainers/similarity/backends/tensorflow/base.py
imports: from typing import Any, Callable, List, Optional, Union
  import numpy
  import tensorflow
  from tensorflow import keras
label_desc: Use image node_id 1 for calling the _TensorFlowBackend obj's underlying member method code with example usage: obj.get_grads(model, X, Y, loss_fn) and returns: grad_X_train