class_docstr: Implementation of T-Few (https://arxiv.org/pdf/2205.05638.pdf).
  Refer to https://github.com/r-three/t-few
class_docstr_tok: ["Implementation","of","T-Few","(","https",":","\/\/arxiv.org\/pdf\/2205.05638.pdf",")",".","Refer","to","https",":","\/\/github.com\/r-three\/t-few"]
code: def get_output_dict(
      self,
      features: torch.Tensor,
      logits: torch.Tensor,
      target_template_logits: torch.Tensor,
      lm_target: torch.Tensor,
      column_features: Optional[Dict[str, torch.Tensor]] = None,
      column_feature_masks: Optional[Dict[str, torch.Tensor]] = None,
  ):
      ret = {COLUMN_FEATURES: {FEATURES: {}, MASKS: {}}}
      if column_features != None:
          ret[COLUMN_FEATURES][FEATURES].update(column_features)
          ret[COLUMN_FEATURES][MASKS].update(column_feature_masks)
  
      ret.update(
          {
              LOGITS: logits,  # needed for default crossentropy loss
              TEMPLATE_LOGITS: target_template_logits,  # needed for unlikelihood loss
              LM_TARGET: lm_target,  # needed for lm loss
              FEATURES: features,
          }
      )
  
      return {self.prefix: ret}
  
imports: import collections
  import logging
  import os
  import random
  from functools import lru_cache
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from transformers import AutoConfig, AutoModelForSeq2SeqLM
  from transformers import logging
  from ..constants import AUTOMM, CHOICES_IDS, COLUMN, COLUMN_FEATURES, FEATURES, LABEL, LM_TARGET, LOGITS, MASKS, TEMPLATE_LOGITS, TEXT_SEGMENT_IDS, TEXT_TOKEN_IDS, TEXT_VALID_LENGTH
  from .utils import DummyLayer, assign_layer_ids, get_column_features, get_pretrained_tokenizer
label_desc: Use image node_id 11 for calling the TFewModel obj's underlying member method code with example usage: obj.get_output_dict(features, logits, target_template_logits, lm_target, column_features, column_feature_masks) and returns: dict