code: def _infer_input_ids(
      self, sample_list: Dict[str, Tensor]
  ) -> Dict[str, Tensor]:
      # Input IDs (or text tokens/image features)
      input_ids: Dict[str, Tensor] = {}
      current_text_idx = 0
      for idx, encoder in enumerate(self.encoders.values()):
          modality = self.modality_keys[idx]
          if self.modality_type[idx] == "text":
              # First, check if standard input_ids corresponds to text
              # if not, check for modality key inside the sample list
              text_ids = self._check_keys_for_modality(
                  sample_list, ("input_ids", modality)
              )
              # This handles the case of more than one text modalities
              # with type text. The input_ids must be stacked in this case.
              # For example, if there are two text modalities, input ids should
              # look have shape: B X 2 X L where second dim points to stacked
              # text ids. Furthermore, make sure that the sequence of modalities
              # in config is same as the sequence in the stacked input ids.
              if text_ids.dim() > 2:
                  input_ids[modality] = text_ids[:, current_text_idx]
                  current_text_idx += 1
              else:
                  input_ids[modality] = text_ids
          elif self.modality_type[idx] == "image":
              # input_modal is originally used by MMBT, added for
              # cross-compatibility of interops and datasets.
              input_ids[modality] = self._check_keys_for_modality(
                  sample_list,
                  (modality, "image", "input_modal", "image_feature_0"),
              )
          else:
              # TODO: Later deliberate if missing modalities should
              # be supported in MMFT.
              input_ids[modality] = self._check_keys_for_modality(
                  sample_list, (modality,)
              )
          # In the other case feature will be skipped, as it is not present in
          # the sample list
          if encoder is not None:
              input_ids[modality] = encoder(input_ids[modality])
  
          # For a feature which is of shape B X D and
          # is not text (which is B X L converted later by embeddings to B X L X D)
          # We convert it to B X 1 X D to signify single position dim.
          if (
              self.modality_type[idx] != "text"
              and input_ids[modality].dim() == 2
          ):
              input_ids[modality] = input_ids[modality].unsqueeze(1)
  
      return input_ids
  
imports: import logging
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  import torch
  from mmf.common.registry import registry
  from mmf.models.transformers.base import BaseTransformer, BaseTransformerBackendConfig, BaseTransformerHead, BaseTransformerModalityConfig
  from mmf.models.transformers.heads.mlp import MLP
  from mmf.modules.encoders import ResNet152ImageEncoder
  from mmf.utils.build import build_encoder
  from omegaconf import MISSING, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 7 for calling the MMFTransformer obj's underlying member method code with example usage: obj._infer_input_ids(sample_list) and returns: input_ids