code: def _generate_cow_renders(
      *,
      num_views: int,
      mesh: Meshes,
      azimuth_range: float,
      distance: float,
      resolution: int,
      device: torch.device,
      use_point_light: bool,
  ) -> Tuple[CamerasBase, torch.Tensor, torch.Tensor]:
      """
      Returns:
          cameras: A batch of `num_views` `FoVPerspectiveCameras` from which the
              images are rendered.
          images: A tensor of shape `(num_views, height, width, 3)` containing
              the rendered images.
          silhouettes: A tensor of shape `(num_views, height, width)` containing
              the rendered silhouettes.
      """
  
      # Load obj file
  
      # We scale normalize and center the target mesh to fit in a sphere of radius 1
      # centered at (0,0,0). (scale, center) will be used to bring the predicted mesh
      # to its original center and scale.  Note that normalizing the target mesh,
      # speeds up the optimization but is not necessary!
      verts = mesh.verts_packed()
      N = verts.shape[0]
      center = verts.mean(0)
      scale = max((verts - center).abs().max(0)[0])
      mesh.offset_verts_(-(center.expand(N, 3)))
      mesh.scale_verts_((1.0 / float(scale)))
  
      # Get a batch of viewing angles.
      elev = torch.linspace(0, 0, num_views)  # keep constant
      azim = (
          torch.linspace(-azimuth_range, azimuth_range, num_views)
          + 180.0
      )
  
      # Place a point light in front of the object. As mentioned above, the front of
      # the cow is facing the -z direction.
      if use_point_light:
          lights = PointLights(
              device=device, location=[[0.0, 0.0, -3.0]]
          )
      else:
          lights = AmbientLights(device=device)
  
      # Initialize a perspective camera that represents a batch of different
      # viewing angles. All the cameras helper methods support mixed type inputs and
      # broadcasting. So we can view the camera from a fixed distance, and
      # then specify elevation and azimuth angles for each viewpoint as tensors.
      R, T = look_at_view_transform(dist=distance, elev=elev, azim=azim)
      cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
  
      # Define the settings for rasterization and shading.
      # As we are rendering images for visualization
      # purposes only we will set faces_per_pixel=1 and blur_radius=0.0. Refer to
      # rasterize_meshes.py for explanations of these parameters.  We also leave
      # bin_size and max_faces_per_bin to their default values of None, which sets
      # their values using heuristics and ensures that the faster coarse-to-fine
      # rasterization method is used.  Refer to docs/notes/renderer.md for an
      # explanation of the difference between naive and coarse-to-fine rasterization.
      raster_settings = RasterizationSettings(
          image_size=resolution, blur_radius=0.0, faces_per_pixel=1
      )
  
      # Create a Phong renderer by composing a rasterizer and a shader. The textured
      # Phong shader will interpolate the texture uv coordinates for each vertex,
      # sample from a texture image and apply the Phong lighting model
      blend_params = BlendParams(
          sigma=1e-4, gamma=1e-4, background_color=(0.0, 0.0, 0.0)
      )
      rasterizer_type = MeshRasterizer
      renderer = MeshRendererWithFragments(
          rasterizer=rasterizer_type(
              cameras=cameras, raster_settings=raster_settings
          ),
          shader=HardPhongShader(
              device=device,
              cameras=cameras,
              lights=lights,
              blend_params=blend_params,
          ),
      )
  
      # Create a batch of meshes by repeating the cow mesh and associated textures.
      # Meshes has a useful `extend` method which allows us do this very easily.
      # This also extends the textures.
      meshes = mesh.extend(num_views)
  
      # Render the cow mesh from each viewing angle
      target_images, fragments = renderer(
          meshes, cameras=cameras, lights=lights
      )
      silhouette_binary = (fragments.pix_to_face[..., 0] >= 0).float()
  
      return cameras, target_images[..., :3], silhouette_binary
  
file_path: pytorch3d/pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py
imports: from os.path import dirname, join, realpath
  from typing import Optional, Tuple
  import torch
  from pytorch3d.implicitron.tools.config import registry, run_auto_creation
  from pytorch3d.io import IO
  from pytorch3d.renderer import AmbientLights, BlendParams, CamerasBase, FoVPerspectiveCameras, HardPhongShader, look_at_view_transform, MeshRasterizer, MeshRendererWithFragments, PointLights, RasterizationSettings
  from pytorch3d.structures.meshes import Meshes
  from .dataset_map_provider import DatasetMap, DatasetMapProviderBase, PathManagerFactory
  from .single_sequence_dataset import SingleSceneDataset
  from .utils import DATASET_TYPE_KNOWN
label_desc: Use image node_id 1 for calling a global function with example usage: _generate_cow_renders() and returns: cameras,  target_images,  silhouette_binary