code: def construct_nerf(
      key,
      config: configs.ModelConfig,
      batch_size: int,
      appearance_ids: Sequence[int],
      camera_ids: Sequence[int],
      warp_ids: Sequence[int],
      near: float,
      far: float,
      use_warp_jacobian: bool = False,
      use_weights: bool = False,
  ):
      """Neural Randiance Field.
  
      Args:
        key: jnp.ndarray. Random number generator.
        config: model configs.
        batch_size: the evaluation batch size used for shape inference.
        appearance_ids: the appearance embeddings.
        camera_ids: the camera embeddings.
        warp_ids: the warp embeddings.
        near: the near plane of the scene.
        far: the far plane of the scene.
        use_warp_jacobian: if True the model computes and returns the Jacobian of
          the warped points.
        use_weights: if True return the density weights from the NeRF.
  
      Returns:
        model: nn.Model. Nerf model with parameters.
        state: flax.Module.state. Nerf model state for stateful parameters.
      """
      num_nerf_point_freqs = config.num_nerf_point_freqs
      num_nerf_viewdir_freqs = config.num_nerf_viewdir_freqs
      num_coarse_samples = config.num_coarse_samples
      num_fine_samples = config.num_fine_samples
      use_viewdirs = config.use_viewdirs
      noise_std = config.noise_std
      use_stratified_sampling = config.use_stratified_sampling
      use_white_background = config.use_white_background
      nerf_trunk_depth = config.nerf_trunk_depth
      nerf_trunk_width = config.nerf_trunk_width
      nerf_rgb_branch_depth = config.nerf_rgb_branch_depth
      nerf_rgb_branch_width = config.nerf_rgb_branch_width
      nerf_skips = config.nerf_skips
      alpha_channels = config.alpha_channels
      rgb_channels = config.rgb_channels
      use_linear_disparity = config.use_linear_disparity
  
      model = NerfModel(
          num_coarse_samples=num_coarse_samples,
          num_fine_samples=num_fine_samples,
          use_viewdirs=use_viewdirs,
          near=near,
          far=far,
          noise_std=noise_std,
          nerf_trunk_depth=nerf_trunk_depth,
          nerf_trunk_width=nerf_trunk_width,
          nerf_rgb_branch_depth=nerf_rgb_branch_depth,
          nerf_rgb_branch_width=nerf_rgb_branch_width,
          use_alpha_condition=config.use_alpha_condition,
          use_rgb_condition=config.use_rgb_condition,
          activation=config.activation,
          sigma_activation=config.sigma_activation,
          nerf_skips=nerf_skips,
          alpha_channels=alpha_channels,
          rgb_channels=rgb_channels,
          use_stratified_sampling=use_stratified_sampling,
          use_white_background=use_white_background,
          use_sample_at_infinity=config.use_sample_at_infinity,
          num_nerf_point_freqs=num_nerf_point_freqs,
          num_nerf_viewdir_freqs=num_nerf_viewdir_freqs,
          use_linear_disparity=use_linear_disparity,
          use_warp_jacobian=use_warp_jacobian,
          use_weights=use_weights,
          use_appearance_metadata=config.use_appearance_metadata,
          use_camera_metadata=config.use_camera_metadata,
          use_warp=config.use_warp,
          appearance_ids=appearance_ids,
          camera_ids=camera_ids,
          warp_ids=warp_ids,
          num_appearance_features=config.appearance_metadata_dims,
          num_camera_features=config.camera_metadata_dims,
          num_warp_freqs=config.num_warp_freqs,
          num_warp_features=config.num_warp_features,
          warp_field_type=config.warp_field_type,
          warp_metadata_encoder_type=config.warp_metadata_encoder_type,
          warp_kwargs=immutabledict.immutabledict(config.warp_kwargs),
      )
  
      init_rays_dict = {
          "origins": jnp.ones((batch_size, 3), jnp.float32),
          "directions": jnp.ones((batch_size, 3), jnp.float32),
          "metadata": {
              "warp": jnp.ones((batch_size, 1), jnp.uint32),
              "camera": jnp.ones((batch_size, 1), jnp.uint32),
              "appearance": jnp.ones((batch_size, 1), jnp.uint32),
              "time": jnp.ones((batch_size, 1), jnp.float32),
          },
      }
      warp_extra = {
          "alpha": 0.0,
          "time_alpha": 0.0,
      }
  
      key, key1, key2 = random.split(key, 3)
      params = model.init(
          {"params": key, "coarse": key1, "fine": key2},
          init_rays_dict,
          warp_extra=warp_extra,
      )["params"]
  
      return model, params
  
imports: from typing import Any, Dict, Mapping, Optional, Tuple, Sequence
  from flax import linen
  import immutabledict
  from jax import random
  import jax.numpy
  from nerfies import configs
  from nerfies import glo
  from nerfies import model_utils
  from nerfies import modules
  from nerfies import types
  from nerfies import warping
label_desc: Use image node_id 1 for calling a global function with example usage: construct_nerf(key, config, batch_size, appearance_ids, camera_ids, warp_ids, near, far, use_warp_jacobian, use_weights) and returns: model,  params