code: def _rebuild_sparse_tensor(layout, data):
      """
      Rebuilds a sparse tensor from its sparse storage representation.
  
      Args:
          layout (str): The sparse storage layout of the tensor.
          data (tuple): The tensor's sparse storage representation.
      """
      if layout == torch.sparse_coo:
          if len(data) == 3:
              # For BC:
              indices, values, size = data
              is_coalesced = None
          else:
              indices, values, size, is_coalesced = data
          result = torch.sparse_coo_tensor(
              indices,
              values,
              size,
              check_invariants=False,
              is_coalesced=is_coalesced,
          )
          _sparse_tensors_to_validate.append(result)
          return result
  
      elif layout in {
          torch.sparse_csr,
          torch.sparse_csc,
          torch.sparse_bsr,
          torch.sparse_bsc,
      }:
          compressed_indices, plain_indices, values, size = data
          result = torch.sparse_compressed_tensor(
              compressed_indices,
              plain_indices,
              values,
              size,
              layout=layout,
              check_invariants=False,
          )
          _sparse_tensors_to_validate.append(result)
          return result
  
      raise NotImplementedError(
          f"rebuilding sparse tensor for layout {layout}"
      )
  
imports: import copyreg
  import functools
  import sys
  import traceback
  import warnings
  from collections import defaultdict
  from typing import Any, DefaultDict, List, Optional
  import torch
label_desc: Use image node_id 11 for calling a global function with example usage: _rebuild_sparse_tensor(layout, data) and returns: result, result