code: def grid(
      height: int,
      width: int,
      dtype: Optional[torch.dtype] = None,
      device: Optional[torch.device] = None,
  ) -> Tuple[Tensor, Tensor]:
      r"""Returns the edge indices of a two-dimensional grid graph with height
      :attr:`height` and width :attr:`width` and its node positions.
  
      Args:
          height (int): The height of the grid.
          width (int): The width of the grid.
          dtype (torch.dtype, optional): The desired data type of the returned
              position tensor. (default: :obj:`None`)
          device (torch.device, optional): The desired device of the returned
              tensors. (default: :obj:`None`)
  
      :rtype: (:class:`LongTensor`, :class:`Tensor`)
  
      Example:
          >>> (row, col), pos = grid(height=2, width=2)
          >>> row
          tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3])
          >>> col
          tensor([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3])
          >>> pos
          tensor([[0., 1.],
                  [1., 1.],
                  [0., 0.],
                  [1., 0.]])
      """
      edge_index = grid_index(height, width, device)
      pos = grid_pos(height, width, dtype, device)
      return edge_index, pos
  
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch_geometric.utils import coalesce
label_desc: Use image node_id 1 for calling a global function with example usage: grid(height, width, dtype, device) and returns: edge_index,  pos