code: def flatten_and_batch_shift_indices(
      indices: torch.Tensor, sequence_length: int
  ) -> torch.Tensor:
      """
      This is a subroutine for [`batched_index_select`](./util.md#batched_index_select).
      The given `indices` of size `(batch_size, d_1, ..., d_n)` indexes into dimension 2 of a
      target tensor, which has size `(batch_size, sequence_length, embedding_size)`. This
      function returns a vector that correctly indexes into the flattened target. The sequence
      length of the target must be provided to compute the appropriate offsets.
      ```python
          indices = torch.ones([2,3], dtype=torch.long)
          # Sequence length of the target tensor.
          sequence_length = 10
          shifted_indices = flatten_and_batch_shift_indices(indices, sequence_length)
          # Indices into the second element in the batch are correctly shifted
          # to take into account that the target tensor will be flattened before
          # the indices are applied.
          assert shifted_indices == [1, 1, 1, 11, 11, 11]
      ```
      # Parameters
      indices : `torch.LongTensor`, required.
      sequence_length : `int`, required.
          The length of the sequence the indices index into.
          This must be the second dimension of the tensor.
      # Returns
      offset_indices : `torch.LongTensor`
      """
      # Shape: (batch_size)
      if (
          torch.max(indices) >= sequence_length
          or torch.min(indices) < 0
      ):
          raise IndexError(
              f"All elements in indices should be in range (0, {sequence_length - 1})"
          )
      offsets = (
          get_range_vector(indices.size(0), get_device_of(indices))
          * sequence_length
      )
      for _ in range(len(indices.size()) - 1):
          offsets = offsets.unsqueeze(1)
  
      # Shape: (batch_size, d_1, ..., d_n)
      offset_indices = indices + offsets
  
      # Shape: (batch_size * d_1 * ... * d_n)
      offset_indices = offset_indices.view(-1)
      return offset_indices
  
imports: from typing import Any, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: flatten_and_batch_shift_indices(indices, sequence_length) and returns: offset_indices