class_docstr: An `InfluenceInterpreter` interprets an AllenNLP models's outputs by finding the
  training instances that had the most influence on the prediction for each test input.
  
  See [Understanding Black-box Predictions via Influence Functions]
  (https://api.semanticscholar.org/CorpusID:13193974) for more information.
  
  Subclasses are required to implement the `_calculate_influence_scores()` method.
  
  # Parameters
  
  model : `Model`, required
  
  train_data_path : `DatasetReaderInput`, required
  
  train_dataset_reader : `DatasetReader`, required
  
  test_dataset_reader : `Optional[DatasetReader]`, optional (default = `None`)
      This is the dataset reader to read the test set file. If not provided, the
      `train_dataset_reader` is used.
  
  train_data_loader : `Lazy[DataLoader]`, optional (default = `Lazy(SimpleDataLoader)`)
      The data loader used to load training instances.
  
      !!! Note
          This data loader is only used to call `DataLoader.iter_instances()`, so certain
          `DataLoader` settings like `batch_size` will have no effect.
  
  test_data_loader : `Lazy[DataLoader]`, optional (default = `Lazy(SimpleDataLoader)`)
      The data loader used to load test instances when `interpret_from_file()` is called.
  
      !!! Note
          Like `train_data_loader`, this data loader is only used to call `DataLoader.iter_instances()`,
          so certain `DataLoader` settings like `batch_size` will have no effect.
  
  params_to_freeze : `Optional[List[str]]`, optional (default = `None`)
      An optional list of strings, each of which should be a regular expression that matches
      some parameter keys of the model. Any matching parameters will be have `requires_grad`
      set to `False`.
  
  cuda_device : `int`, optional (default = `-1`)
      The index of GPU device we want to calculate scores on. If not provided, we uses `-1`
      which correspond to using CPU.
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code: def _gather_train_instances_and_compute_gradients(self) -> None:
      logger.info(
          "Gathering training instances and computing gradients. "
          "The result will be cached so this only needs to be done once."
      )
      self._train_instances = []
      self.model.train()
      for instance in Tqdm.tqdm(
          self._train_loader.iter_instances(),
          desc="calculating training gradients",
      ):
          batch = Batch([instance])
          batch.index_instances(self.vocab)
          tensor_dict = move_to_device(
              batch.as_tensor_dict(), self.device
          )
  
          self.model.zero_grad()
  
          # Compute loss with respect to the test instance.
          output_dict = self.model(**tensor_dict)
          loss = output_dict["loss"]
  
          if (
              self._used_params is None
              or self._used_param_names is None
          ):
              self._used_params = []
              self._used_param_names = []
              # we only know what parameters in the models requires gradient after
              # we do the first .backward() and we store those used parameters
              loss.backward(retain_graph=True)
              for name, param in self.model.named_parameters():
                  if param.requires_grad and param.grad is not None:
                      self._used_params.append(param)
                      self._used_param_names.append(name)
  
          # Get gradients.
          grads = autograd.grad(loss, self._used_params)
          # Sanity check.
          assert len(grads) == len(self._used_params)
  
          self._train_instances.append(
              InstanceWithGrads(
                  instance=instance,
                  loss=loss.detach().item(),
                  grads=grads,
              )
          )
  
imports: import logging
  from os import PathLike
  import re
  from typing import List, Optional, NamedTuple, Sequence, Union, Dict, Any
  import torch
  from torch import autograd
  from allennlp.common import Registrable, Lazy, plugins
  from allennlp.common.tqdm import Tqdm
  from allennlp.common.util import int_to_device
  from allennlp.data import Instance, DatasetReader, DatasetReaderInput, Batch
  from allennlp.data.data_loaders import DataLoader, SimpleDataLoader
  from allennlp.models import Model, Archive, load_archive
  from allennlp.nn.util import move_to_device
label_desc: Use image node_id 11 for calling the InfluenceInterpreter obj's underlying member method code with example usage: obj._gather_train_instances_and_compute_gradients() without return types