class_docstr: Decoding function is a torch.nn.Module which takes the embedding of a location in
  space and transforms it into the required quantity (for example density and color).
docstr: Args:
      features (torch.Tensor): tensor of shape (batch, ..., num_in_features)
      z: optional tensor to append to parts of the decoding function
  Returns:
      decoded_features (torch.Tensor) : tensor of
          shape (batch, ..., num_out_features)
code: def forward(
      self, features: torch.Tensor, z: Optional[torch.Tensor] = None
  ) -> torch.Tensor:
      """
      Args:
          features (torch.Tensor): tensor of shape (batch, ..., num_in_features)
          z: optional tensor to append to parts of the decoding function
      Returns:
          decoded_features (torch.Tensor) : tensor of
              shape (batch, ..., num_out_features)
      """
      raise NotImplementedError()
  
imports: import logging
  from dataclasses import field
  from enum import Enum
  from typing import Dict, Optional, Tuple
  import torch
  from omegaconf import DictConfig
  from pytorch3d.implicitron.tools.config import Configurable, registry, ReplaceableBase, run_auto_creation
label_desc: Use image node_id 1 for calling the DecoderFunctionBase obj's underlying member method code with example usage: obj.forward(features, z) without return types