code: def narrow(
      src: SparseTensor, dim: int, start: int, length: int
  ) -> SparseTensor:
      if dim < 0:
          dim = src.dim() + dim
  
      if start < 0:
          start = src.size(dim) + start
  
      if dim == 0:
          rowptr, col, value = src.csr()
  
          rowptr = rowptr.narrow(0, start=start, length=length + 1)
          row_start = rowptr[0]
          rowptr = rowptr - row_start
          row_length = rowptr[-1]
  
          row = src.storage._row
          if row is not None:
              row = row.narrow(0, row_start, row_length) - start
  
          col = col.narrow(0, row_start, row_length)
  
          if value is not None:
              value = value.narrow(0, row_start, row_length)
  
          sparse_sizes = (length, src.sparse_size(1))
  
          rowcount = src.storage._rowcount
          if rowcount is not None:
              rowcount = rowcount.narrow(0, start=start, length=length)
  
          storage = SparseStorage(
              row=row,
              rowptr=rowptr,
              col=col,
              value=value,
              sparse_sizes=sparse_sizes,
              rowcount=rowcount,
              colptr=None,
              colcount=None,
              csr2csc=None,
              csc2csr=None,
              is_sorted=True,
          )
          return src.from_storage(storage)
  
      elif dim == 1:
          # This is faster than accessing `csc()` contrary to the `dim=0` case.
          row, col, value = src.coo()
          mask = (col >= start) & (col < start + length)
  
          row = row[mask]
          col = col[mask] - start
  
          if value is not None:
              value = value[mask]
  
          sparse_sizes = (src.sparse_size(0), length)
  
          colptr = src.storage._colptr
          if colptr is not None:
              colptr = colptr.narrow(0, start=start, length=length + 1)
              colptr = colptr - colptr[0]
  
          colcount = src.storage._colcount
          if colcount is not None:
              colcount = colcount.narrow(0, start=start, length=length)
  
          storage = SparseStorage(
              row=row,
              rowptr=None,
              col=col,
              value=value,
              sparse_sizes=sparse_sizes,
              rowcount=None,
              colptr=colptr,
              colcount=colcount,
              csr2csc=None,
              csc2csr=None,
              is_sorted=True,
          )
          return src.from_storage(storage)
  
      else:
          value = src.storage.value()
          if value is not None:
              return src.set_value(
                  value.narrow(dim - 1, start, length), layout="coo"
              )
          else:
              raise ValueError
  
imports: from typing import Tuple
  from torch_sparse.storage import SparseStorage
  from torch_sparse.tensor import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: narrow(src, dim, start, length) and returns: src, src, src