code: def construct_full_unpacked_stream(
      num_real_text_tokens: Union[List[List[int]], "torch.Tensor"],
      input_stream: "torch.Tensor",
      image_tokens: List[List["torch.Tensor"]],
      batch_size: int,
      num_sub_sequences: int,
  ) -> List["torch.Tensor"]:
      """Takes an input_stream tensor of shape B x S x ?. For each subsequence, adds any required
      padding to account for images and then unpacks the subsequences to create a single sequence per item in the batch.
      Returns a list of tensors, one for each item in the batch."""
  
      all_bi_stream = []
  
      for batch_index in range(batch_size):
          all_si_stream = []
  
          # First, construct full token stream (including image placeholder tokens) and loss mask for each subsequence
          # and append to lists. We use lists rather than tensors because each subsequence is variable-sized.
          # TODO Remove this logic in a subsequent release since subsequences are not supported.
          image_adjustment = image_tokens[batch_index][0]
          subsequence_stream = torch.cat(
              [image_adjustment, input_stream[batch_index, 0]], dim=0
          )
          num_real_tokens = (
              image_adjustment.shape[0]
              + num_real_text_tokens[batch_index][0]
          )
          all_si_stream.append(subsequence_stream[:num_real_tokens])
          all_bi_stream.append(torch.cat(all_si_stream, dim=0))
  
      return all_bi_stream
  
imports: import re
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import PaddingStrategy, TruncationStrategy
  from ...utils import TensorType, is_torch_available, logging, requires_backends
label_desc: Use image node_id 2 for calling a global function with example usage: construct_full_unpacked_stream(num_real_text_tokens, input_stream, image_tokens, batch_size, num_sub_sequences) and returns: all_bi_stream