class_docstr: Learn a weight per channel
  - used to get "fake" previous scene to encode the first one.
code: def __init__(self, num_channels: int) -> None:
      super().__init__()
      self.weight = nn.parameter.Parameter(
          torch.rand((1, num_channels, 1, 1))
      )
  
imports: import itertools
  import math
  from typing import Generator, List, NamedTuple, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn
  import utils.memoize
  from datamodules.video_data_api import Scenes, VideoData
  from neural.entropy_model import PreviousLatent, VCTEntropyModel
  from torch import Tensor
label_desc: Use image node_id 1 to create a new PerChannelWeight object from inherited base classes: nn with example: obj = PerChannelWeight(num_channels)