code: def normalized_pixel_coordinates(
      image_width: int, image_height: int
  ) -> Tuple[tf.Tensor, tf.Tensor]:
      """Computes the normalized pixel coordinates for the specified image size.
  
      The x-coordinates will range from -1 to 1 left to right.
      The y-coordinates will range from -1 to 1 top to bottom.
      The extrema +-1 will fall onto the exterior pixel boundaries, while the
      coordinates will be evaluated at pixel centers. So, image of width 4 will have
      normalized pixel x-coordinates at [-0.75 -0.25 0.25 0.75], while image of
      width 3 will have them at [-0.667 0 0.667].
  
      Args:
        image_width: int specifying desired output image width in pixels.
        image_height: int specifying desired output image height in pixels.
  
      Returns:
        Two float32 tensors with shape [image_height, image_width] containing x- and
        y- coordinates, respecively, for each image pixel.
      """
      width = tf.cast(image_width, tf.float32)
      height = tf.cast(image_height, tf.float32)
      x_range = (2 * tf.range(width) + 1) / width - 1
      y_range = (2 * tf.range(height) + 1) / height - 1
      x_coords, y_coords = tf.meshgrid(x_range, y_range)
      return x_coords, y_coords
  
file_path: graphics/tensorflow_graphics/rendering/barycentrics.py
imports: from typing import Tuple
  import tensorflow
  from tensorflow_graphics.rendering import framebuffer
  from tensorflow_graphics.util import shape
  from tensorflow_graphics.util import type_alias
label_desc: Use image node_id 2 for calling a global function with example usage: normalized_pixel_coordinates(image_width, image_height) and returns: x_coords,  y_coords