code: def backward(ctx, grad):
      (
          b,
          row_indices,
          values,
          row_offsets,
          column_indices,
          *_transp_info,
      ) = ctx.saved_tensors
      k = b.shape[1]
  
      # gradients w.r.t. values
      grad = grad.contiguous()
  
      grad_sparse = _sddmm_func(
          grad, b, row_indices, row_offsets, column_indices
      )
  
      (
          row_indices_t,
          values_t,
          row_offsets_t,
          column_indices_t,
      ) = _transpose_with_info(values, _transp_info)
  
      grad_dense = torch.ops.xformers.spmm_sputnik(
          grad,
          row_indices_t,
          values_t,
          row_offsets_t,
          column_indices_t,
          k,
      )
  
      return grad_dense, None, grad_sparse, None, None, None, None
  
file_path: xformers/xformers/sparse/_csr_ops.py
imports: import torch
  from .utils import _csr_to_coo, _transpose_with_info
label_desc: Use image node_id 2 for calling the _spmm obj's underlying member method code with example usage: obj.backward(ctx, grad) and returns: grad_dense,  None,  grad_sparse,  None,  None,  None,  None