seq (sequence of Tensors)
Example:
  >>> x = torch.tensor([1, 2, 3])
  >>> y = torch.tensor([4, 5, 6])

  Observe the element-wise pairings across the grid, (1, 4),
  (1, 5), ..., (3, 6). This is the same thing as the
  cartesian product.
  >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
  >>> grid_x
  tensor([[1, 1, 1],
          [2, 2, 2],
          [3, 3, 3]])
  >>> grid_y
  tensor([[4, 5, 6],
          [4, 5, 6],
          [4, 5, 6]])

  This correspondence can be seen when these grids are
  stacked properly.
  >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
  ...             torch.cartesian_prod(x, y))
  True

  `torch.meshgrid` is commonly used to produce a grid for
  plotting.
  >>> import matplotlib.pyplot as plt
  >>> xs = torch.linspace(-5, 5, steps=100)
  >>> ys = torch.linspace(-5, 5, steps=100)
  >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
