class_docstr: Renders a novel synthesis view of a trained NeRF model for given camera.
docstr: Renders a novel synthesis view of a trained NeRF model for given camera.
  
  Args:
      camera: camera for image rendering: PinholeCamera.
  
  Returns:
      Rendered image with shape :math:`(H, W, 3)`.
docstr_tok: ["Renders","a","novel","synthesis","view","of","a","trained","NeRF","model","for","given","camera",".","Args",":","camera",":","camera","for","image","rendering",":","PinholeCamera",".","Returns",":","Rendered","image","with","shape",":","math",":","`","(","H",",","W",",","3",")","`","."]
code: def render_view(self, camera: PinholeCamera) -> Tensor:
      """Renders a novel synthesis view of a trained NeRF model for given camera.
  
      Args:
          camera: camera for image rendering: PinholeCamera.
  
      Returns:
          Rendered image with shape :math:`(H, W, 3)`.
      """
      # create ray for this camera
      rays: Ray = self._create_rays(camera)
  
      # render the image
      with torch_inference_mode():
          rgb_model = self._nerf_model(rays.origin, rays.direction)
  
      rgb_image = rgb_model.view(
          self._image_size[0], self._image_size[1], 3
      )
  
      return rgb_image
  
imports: from __future__ import annotations
  import torch
  from torch import nn
  from torch.nn import functional
  from kornia.core import Module, Tensor
  from kornia.geometry.camera import PinholeCamera
  from kornia.geometry.ray import Ray
  from kornia.nerf.positional_encoder import PositionalEncoder
  from kornia.nerf.samplers import sample_lengths, sample_ray_points
  from kornia.nerf.volume_renderer import IrregularRenderer, RegularRenderer
  from kornia.utils._compat import torch_inference_mode
  from kornia.utils.grid import create_meshgrid
label_desc: Use image node_id 3 for calling the NerfModelRenderer obj's underlying member method code with example usage: obj.render_view(camera) and returns: rgb_image