code: def degree(
      index: Tensor,
      num_nodes: Optional[int] = None,
      dtype: Optional[torch.dtype] = None,
  ) -> Tensor:
      r"""Computes the (unweighted) degree of a given one-dimensional index
      tensor.
  
      Args:
          index (LongTensor): Index tensor.
          num_nodes (int, optional): The number of nodes, *i.e.*
              :obj:`max_val + 1` of :attr:`index`. (default: :obj:`None`)
          dtype (:obj:`torch.dtype`, optional): The desired data type of the
              returned tensor.
  
      :rtype: :class:`Tensor`
  
      Example:
          >>> row = torch.tensor([0, 1, 0, 2, 0])
          >>> degree(row, dtype=torch.long)
          tensor([3, 1, 1])
      """
      N = maybe_num_nodes(index, num_nodes)
      out = torch.zeros((N,), dtype=dtype, device=index.device)
      one = torch.ones(
          (index.size(0),), dtype=out.dtype, device=out.device
      )
      return out.scatter_add_(0, index, one)
  
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch_geometric.utils.num_nodes import maybe_num_nodes
label_desc: Use image node_id 1 for calling a global function with example usage: degree(index, num_nodes, dtype) and returns: out