code: def sparse_reshape(self, num_rows: int, num_cols: int):
      assert num_rows > 0 or num_rows == -1
      assert num_cols > 0 or num_cols == -1
      assert num_rows > 0 or num_cols > 0
  
      total = self.sparse_size(0) * self.sparse_size(1)
  
      if num_rows == -1:
          num_rows = total // num_cols
  
      if num_cols == -1:
          num_cols = total // num_rows
  
      assert num_rows * num_cols == total
  
      idx = self.sparse_size(1) * self.row() + self.col()
  
      row = torch.div(idx, num_cols, rounding_mode="floor")
      col = idx % num_cols
      assert row.dtype == torch.long and col.dtype == torch.long
  
      return SparseStorage(
          row=row,
          rowptr=None,
          col=col,
          value=self._value,
          sparse_sizes=(num_rows, num_cols),
          rowcount=None,
          colptr=None,
          colcount=None,
          csr2csc=None,
          csc2csr=None,
          is_sorted=True,
          trust_data=True,
      )
  
imports: import warnings
  from typing import List, Optional, Tuple
  import torch
  from torch_scatter import scatter_add, segment_csr
  from torch_sparse.utils import Final, index_sort
label_desc: Use image node_id 15 for calling the SparseStorage obj's underlying member method code with example usage: obj.sparse_reshape(num_rows, num_cols) and returns: SparseStorage