code: def full_unpacked_stream_to_tensor(
      all_bi_tokens_to_place: List[int],
      full_unpacked_stream: List["torch.Tensor"],
      fill_value: int,
      batch_size: int,
      new_seq_len: int,
      offset: int,
  ) -> "torch.Tensor":
      """Takes an unpacked stream of tokens (i.e. a list of tensors, one for each item in the batch) and does
      the required padding to create a single tensor for the batch of shape batch_size x new_seq_len.
      """
  
      assert len(all_bi_tokens_to_place) == batch_size
      assert len(full_unpacked_stream) == batch_size
  
      # Create padded tensors for the full batch.
      new_padded_tensor = torch.full(
          [batch_size, new_seq_len],
          fill_value=fill_value,
          dtype=full_unpacked_stream[0].dtype,
          device=full_unpacked_stream[0].device,
      )
  
      # Place each batch entry into the batch tensor.
      for bi in range(batch_size):
          tokens_to_place = all_bi_tokens_to_place[bi]
          new_padded_tensor[
              bi, :tokens_to_place
          ] = full_unpacked_stream[bi][
              offset : tokens_to_place + offset
          ]
  
      return new_padded_tensor
  
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 1 for calling a global function with example usage: full_unpacked_stream_to_tensor(all_bi_tokens_to_place, full_unpacked_stream, fill_value, batch_size, new_seq_len, offset) and returns: new_padded_tensor