class_docstr: Base class for LLM models
  
  This class handles a few of the checks, as well as the whole prediction
  machinery.
  
  Required attributes are:
  
  - model_name
  - model
  - tokenizer
  - prompt
  - probas_sum_to_1
  - device
  - error_low_prob
  - threshold_low_prob
  - use_caching
class_docstr_tok: ["Base","class","for","LLM","models","This","class","handles","a","few","of","the","checks",",","as","well","as","the","whole","prediction","machinery",".","Required","attributes","are",":","-","model_name","-","model","-","tokenizer","-","prompt","-","probas_sum_to_1","-","device","-","error_low_prob","-","threshold_low_prob","-","use_caching"]
code: def _is_encoder_decoder(self, model):
      return hasattr(model, "get_encoder")
  
imports: import importlib
  import warnings
  from string import Formatter
  import numpy
  import torch
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils import check_random_state
  from sklearn.utils.validation import check_is_fitted
  from transformers import AutoConfig, AutoTokenizer, LogitsProcessor
  from skorch.exceptions import LowProbabilityError
  from skorch.llm.prompts import DEFAULT_PROMPT_FEW_SHOT, DEFAULT_PROMPT_ZERO_SHOT, DELIM
label_desc: Use image node_id 9 for calling the _LlmBase obj's underlying member method code with example usage: obj._is_encoder_decoder(model) and returns: hasattr