class_docstr: Outputs: `Tuple` comprising various elements depending on the configuration
      (config) and inputs:
      **last_hidden_state**: ``torch.FloatTensor`` of shape
          ``(batch_size, sequence_length, hidden_size)``. Sequence of
          hidden-states at the output of the last layer of the model.
      **pooler_output**: ``torch.FloatTensor`` of shape
          ``(batch_size, hidden_size)``. Last layer hidden-state of the
          first token of the sequence (classification token) further processed
          by a Linear layer and a Tanh activation function. The Linear
          layer weights are trained from the next sentence prediction
          (classification) objective during Bert pretraining. This output
          is usually *not* a good summary of the semantic content of the
          input, you're often better with averaging or pooling
          the sequence of hidden-states for the whole input sequence.
      **hidden_states**: (`optional`, returned when
          ``config.output_hidden_states=True``)
          list of ``torch.FloatTensor`` (one for the output of each layer +
          the output of the embeddings)
          of shape ``(batch_size, sequence_length, hidden_size)``:
          Hidden-states of the model at the output of each layer plus the
          initial embedding outputs.
      **attentions**: (`optional`, returned when
          ``config.output_attentions=True``) list of ``torch.FloatTensor``
          (one for each layer) of shape ``(batch_size, num_heads,
          sequence_length, sequence_length)``: Attentions weights after
          the attention softmax, used to compute the weighted average in the
          self-attention heads.
  Examples::
      # For example purposes. Not runnable.
      transformer = BertModel.from_pretrained('bert-base-uncased')
      encoder = ImageEncoder(args)
      mmbt = MMBTModel(config, transformer, encoder)
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code: def set_input_embeddings(self, value):
      self.embeddings.word_embeddings = value
  
imports: import os
  from copy import deepcopy
  from dataclasses import dataclass
  from typing import Dict, Optional, Union
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.models.interfaces.mmbt import MMBTGridHMInterface
  from mmf.modules.encoders import EncoderFactory, ImageEncoderFactory, ImageEncoderTypes, MultiModalEncoderBase, ResNet152ImageEncoder, TextEncoderFactory, TextEncoderTypes, TransformerEncoder
  from mmf.modules.hf_layers import replace_with_jit
  from mmf.utils.checkpoint import load_pretrained_model
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.modeling import get_optimizer_parameters_for_bert
  from omegaconf import DictConfig, II, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 4 for calling the MMBTModel obj's underlying member method code with example usage: obj.set_input_embeddings(value) without return types