class_docstr: Return open multi-dimensional "meshgrid".
  
  LAX-backend implementation of :obj:`numpy.ogrid`. This is a convenience wrapper for
  functionality provided by :func:`jax.numpy.meshgrid` with ``sparse=True``.
  
  See Also:
    jnp.mgrid: dense version of jnp.ogrid
  
  Examples:
    Pass ``[start:stop:step]`` to generate values similar to :func:`jax.numpy.arange`:
  
    >>> jnp.ogrid[0:4:1]
    Array([0, 1, 2, 3], dtype=int32)
  
    Passing an imaginary step generates values similar to :func:`jax.numpy.linspace`:
  
    >>> jnp.ogrid[0:1:4j]
    Array([0.        , 0.33333334, 0.6666667 , 1.        ], dtype=float32)
  
    Multiple slices can be used to create sparse grids of indices:
  
    >>> jnp.ogrid[:2, :3]
    [Array([[0],
            [1]], dtype=int32),
     Array([[0, 1, 2]], dtype=int32)]
code: def __getitem__(
      self, key: slice | tuple[slice, ...]
  ) -> Array | list[Array]:
      if isinstance(key, slice):
          return _make_1d_grid_from_slice(key, op_name="ogrid")
      output: Iterable[Array] = (
          _make_1d_grid_from_slice(k, op_name="ogrid") for k in key
      )
      with jax.numpy_dtype_promotion("standard"):
          output = promote_dtypes(*output)
      return meshgrid(*output, indexing="ij", sparse=True)
  
imports: from __future__ import annotations
  import abc
  from collections.abc import Iterable
  from typing import Any, Union
  import jax
  from jax._src import core
  from jax._src.numpy.util import promote_dtypes
  from jax._src.numpy.lax_numpy import arange, array, concatenate, expand_dims, linspace, meshgrid, stack, transpose
  from jax._src.typing import Array, ArrayLike
  import numpy
label_desc: Use image node_id 1 for calling the _Ogrid obj's underlying member method code with example usage: obj.__getitem__(key) and returns: meshgrid, _make_1d_grid_from_slice