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: Returns the input/embedding layer of the model.
  If the predictor wraps around a non-AllenNLP model,
  this function should be overridden to specify the correct input/embedding layer.
  For the cases where the input layer _is_ an embedding layer, this should be the
  layer 0 of the embedder.
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code: def get_interpretable_layer(self) -> torch.nn.Module:
      """
      Returns the input/embedding layer of the model.
      If the predictor wraps around a non-AllenNLP model,
      this function should be overridden to specify the correct input/embedding layer.
      For the cases where the input layer _is_ an embedding layer, this should be the
      layer 0 of the embedder.
      """
      try:
          return util.find_embedding_layer(self._model)
      except RuntimeError:
          raise RuntimeError(
              "If the model does not use `TextFieldEmbedder`, please override "
              "`get_interpretable_layer` in your predictor to specify the embedding layer."
          )
  
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 7 for calling the Predictor obj's underlying member method code with example usage: obj.get_interpretable_layer() and returns: util