docstr: Forward pass of the model. The input sample_list can be preprocessed using
  the preprocess_sample method which expects to return a
  Dict[str, Dict[str, Tensor]] object. It contains different properties of the
  input modalities and the masks. These can be used to generate embeddings for
  each modality and also create attention mask.
  
  Flow of how the forward pass can be implemented using various modules in
  BaseTransformer:
  
              preprocess_sample                          ||
                      |                                  ||
             generate embeddings                         ||
                      |                                  ||
           generate attention masks                      ||     MODEL
                      |                                  ||
           transformer encoder pass                      ||     FLOW
                      |                                  ||
             different head pass                         ||   DIRECTION
                      |                                  ||
             postprocess_output                          ||
                      |                                  ||
           Dict[str, Tensor] output                      \/
  
  Returns:
      Dict[str, Tensor]: Dict containing scores or losses
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code: def forward(self, sample_list: Dict[str, Any]) -> Dict[str, Tensor]:
      r"""Forward pass of the model. The input sample_list can be preprocessed using
      the preprocess_sample method which expects to return a
      Dict[str, Dict[str, Tensor]] object. It contains different properties of the
      input modalities and the masks. These can be used to generate embeddings for
      each modality and also create attention mask.
  
      Flow of how the forward pass can be implemented using various modules in
      BaseTransformer:
  
                  preprocess_sample                          ||
                          |                                  ||
                 generate embeddings                         ||
                          |                                  ||
               generate attention masks                      ||     MODEL
                          |                                  ||
               transformer encoder pass                      ||     FLOW
                          |                                  ||
                 different head pass                         ||   DIRECTION
                          |                                  ||
                 postprocess_output                          ||
                          |                                  ||
               Dict[str, Tensor] output                      \/
  
      Returns:
          Dict[str, Tensor]: Dict containing scores or losses
      """
      return
  
imports: import logging
  from abc import ABC, abstractmethod
  from dataclasses import asdict, dataclass, field
  from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Type
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.encoders import IdentityEncoder
  from mmf.utils.modeling import get_bert_configured_parameters
  from omegaconf import MISSING, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 13 for calling the BaseTransformer obj's underlying member method code with example usage: obj.forward(sample_list) without return types