class_docstr: a `Predictor` is a thin wrapper around an AllenNLP model that handles JSON -> JSON predictions
  that can be used for serving models through the web API or making predictions in bulk.
docstr: Gets the gradients of the loss with respect to the model inputs.
  
  # Parameters
  
  instances : `List[Instance]`
  
  # Returns
  
  `Tuple[Dict[str, Any], Dict[str, Any]]`
      The first item is a Dict of gradient entries for each input.
      The keys have the form  `{grad_input_1: ..., grad_input_2: ... }`
      up to the number of inputs given. The second item is the model's output.
  
  # Notes
  
  Takes a `JsonDict` representing the inputs of the model and converts
  them to [`Instances`](../data/instance.md)), sends these through
  the model [`forward`](../models/model.md#forward) function after registering hooks on the embedding
  layer of the model. Calls `backward` on the loss and then removes the
  hooks.
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code: def get_gradients(
      self, instances: List[Instance]
  ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
      """
      Gets the gradients of the loss with respect to the model inputs.
  
      # Parameters
  
      instances : `List[Instance]`
  
      # Returns
  
      `Tuple[Dict[str, Any], Dict[str, Any]]`
          The first item is a Dict of gradient entries for each input.
          The keys have the form  `{grad_input_1: ..., grad_input_2: ... }`
          up to the number of inputs given. The second item is the model's output.
  
      # Notes
  
      Takes a `JsonDict` representing the inputs of the model and converts
      them to [`Instances`](../data/instance.md)), sends these through
      the model [`forward`](../models/model.md#forward) function after registering hooks on the embedding
      layer of the model. Calls `backward` on the loss and then removes the
      hooks.
      """
      # set requires_grad to true for all parameters, but save original values to
      # restore them later
      original_param_name_to_requires_grad_dict = {}
      for param_name, param in self._model.named_parameters():
          original_param_name_to_requires_grad_dict[
              param_name
          ] = param.requires_grad
          param.requires_grad = True
  
      embedding_gradients: List[Tensor] = []
      hooks: List[
          RemovableHandle
      ] = self._register_embedding_gradient_hooks(embedding_gradients)
  
      for instance in instances:
          self._dataset_reader.apply_token_indexers(instance)
  
      dataset = Batch(instances)
      dataset.index_instances(self._model.vocab)
      dataset_tensor_dict = util.move_to_device(
          dataset.as_tensor_dict(), self.cuda_device
      )
      # To bypass "RuntimeError: cudnn RNN backward can only be called in training mode"
      with backends.cudnn.flags(enabled=False):
          outputs = self._model.make_output_human_readable(
              self._model.forward(**dataset_tensor_dict)  # type: ignore
          )
  
          loss = outputs["loss"]
          # Zero gradients.
          # NOTE: this is actually more efficient than calling `self._model.zero_grad()`
          # because it avoids a read op when the gradients are first updated below.
          for p in self._model.parameters():
              p.grad = None
          loss.backward()
  
      for hook in hooks:
          hook.remove()
  
      grad_dict = dict()
      for idx, grad in enumerate(embedding_gradients):
          key = "grad_input_" + str(idx + 1)
          grad_dict[key] = grad.detach().cpu().numpy()
  
      # restore the original requires_grad values of the parameters
      for param_name, param in self._model.named_parameters():
          param.requires_grad = (
              original_param_name_to_requires_grad_dict[param_name]
          )
  
      return grad_dict, outputs
  
imports: from typing import List, Iterator, Dict, Tuple, Any, Type, Union, Optional
  import logging
  from os import PathLike
  import json
  import re
  from contextlib import contextmanager
  import numpy
  import torch
  from torch.utils.hooks import RemovableHandle
  from torch import Tensor
  from torch import backends
  from allennlp.common import Registrable, plugins
  from allennlp.common.util import JsonDict, sanitize
  from allennlp.data import DatasetReader, Instance
  from allennlp.data.batch import Batch
  from allennlp.models import Model
  from allennlp.models.archival import Archive, load_archive
  from allennlp.nn import util
label_desc: Use image node_id 6 for calling the Predictor obj's underlying member method code with example usage: obj.get_gradients(instances) and returns: grad_dict,  outputs