class_docstr: Implements a batch, differentiable, standard pinhole camera
class_docstr_tok: ["Implements","a","batch",",","differentiable",",","standard","pinhole","camera"]
docstr: :return: coords of shape (width * height, 2)
docstr_tok: [":","return",":","coords","of","shape","(","width","*","height",",","2",")"]
code: def get_image_coords(self) -> torch.Tensor:
      """
      :return: coords of shape (width * height, 2)
      """
      pixel_indices = torch.arange(self.height * self.width)
      coords = torch.stack(
          [
              pixel_indices % self.width,
              torch.div(
                  pixel_indices, self.width, rounding_mode="trunc"
              ),
          ],
          axis=1,
      )
      return coords
  
file_path: diffusers/src/diffusers/pipelines/shap_e/camera.py
imports: from dataclasses import dataclass
  from typing import Tuple
  import numpy
  import torch
label_desc: Use image node_id 4 for calling the DifferentiableProjectiveCamera obj's underlying member method code with example usage: obj.get_image_coords() and returns: coords