class_docstr: Support huggingface text backbones.
  Refer to https://github.com/huggingface/transformers
class_docstr_tok: ["Support","huggingface","text","backbones",".","Refer","to","https",":","\/\/github.com\/huggingface\/transformers"]
code: def get_output_dict(
      self,
      pooled_features: torch.Tensor,
      logits: 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[LOGITS] = logits
      ret[FEATURES] = pooled_features
      return {self.prefix: ret}
  
imports: import logging
  from typing import Dict, List, Optional, Tuple
  import torch
  from torch import nn
  from transformers import logging
  from transformers.models.t5 import T5PreTrainedModel
  from ..constants import AUTOMM, COLUMN, COLUMN_FEATURES, FEATURES, LABEL, LOGITS, MASKS, TEXT_SEGMENT_IDS, TEXT_TOKEN_IDS, TEXT_VALID_LENGTH
  from .utils import DummyLayer, assign_layer_ids, get_column_features, get_hf_config_and_model, get_pretrained_tokenizer, init_weights
label_desc: Use image node_id 10 for calling the HFAutoModelForTextPrediction obj's underlying member method code with example usage: obj.get_output_dict(pooled_features, logits, column_features, column_feature_masks) and returns: dict