class_docstr: Return dense multi-dimensional "meshgrid".
  
  LAX-backend implementation of :obj:`numpy.mgrid`. This is a convenience wrapper for
  functionality provided by :func:`jax.numpy.meshgrid` with ``sparse=False``.
  
  See Also:
    jnp.ogrid: open/sparse version of jnp.mgrid
  
  Examples:
    Pass ``[start:stop:step]`` to generate values similar to :func:`jax.numpy.arange`:
  
    >>> jnp.mgrid[0:4:1]
    Array([0, 1, 2, 3], dtype=int32)
  
    Passing an imaginary step generates values similar to :func:`jax.numpy.linspace`:
  
    >>> jnp.mgrid[0:1:4j]
    Array([0.        , 0.33333334, 0.6666667 , 1.        ], dtype=float32)
  
    Multiple slices can be used to create broadcasted grids of indices:
  
    >>> jnp.mgrid[:2, :3]
    Array([[[0, 0, 0],
            [1, 1, 1]],
           [[0, 1, 2],
            [0, 1, 2]]], dtype=int32)
code: def __getitem__(self, key: slice | tuple[slice, ...]) -> Array:
      if isinstance(key, slice):
          return _make_1d_grid_from_slice(key, op_name="mgrid")
      output: Iterable[Array] = (
          _make_1d_grid_from_slice(k, op_name="mgrid") for k in key
      )
      with jax.numpy_dtype_promotion("standard"):
          output = promote_dtypes(*output)
      output_arr = meshgrid(*output, indexing="ij", sparse=False)
      if len(output_arr) == 0:
          return arange(0)
      return stack(output_arr, 0)
  
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 _Mgrid obj's underlying member method code with example usage: obj.__getitem__(key) and returns: stack, _make_1d_grid_from_slice, arange