code: def _grid(starts, stops, nums):
      """Generates a M-D uniform axis-aligned grid.
  
      Warning:
        This op is not differentiable. Indeed, the gradient of tf.linspace and
        tf.meshgrid are currently not defined.
  
      Args:
        starts: A tensor of shape `[M]` representing the start points for each
          dimension.
        stops: A tensor of shape `[M]` representing the end points for each
          dimension.
        nums: A tensor of shape `[M]` representing the number of subdivisions for
          each dimension.
  
      Returns:
        A tensor of shape `[nums[0], ..., nums[M-1], M]` containing an M-D uniform
          grid.
      """
      params = [tf.unstack(tensor) for tensor in [starts, stops, nums]]
      layout = [tf.linspace(*param) for param in zip(*params)]
      return tf.stack(tf.meshgrid(*layout, indexing="ij"), axis=-1)
  
file_path: graphics/tensorflow_graphics/geometry/representation/grid.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import zip
  import tensorflow
  from tensorflow_graphics.util import export_api
  from tensorflow_graphics.util import shape
label_desc: Use image node_id 1 for calling a global function with example usage: _grid(starts, stops, nums) and returns: tf