class_docstr: Implement dequantization: feed y' = y + f(z) to the synthesis transform,
  where y is the latent and z is transformer/entropy model features,
docstr: Feed y' = y + f(z) to the synthesis transform
  
  Args:
      num_channels: number of channels, int
      d_model: dimension of the model, int
docstr_tok: ["Feed","y","'","=","y","+","f","(","z",")","to","the","synthesis","transform","Args",":","num_channels",":","number","of","channels",",","int","d_model",":","dimension","of","the","model",",","int"]
code: def __init__(self, num_channels: int, d_model: int) -> None:
      """
      Feed y' = y + f(z) to the synthesis transform
  
      Args:
          num_channels: number of channels, int
          d_model: dimension of the model, int
      """
      super().__init__()
      self._d_model = d_model
      self._num_channels = num_channels
      self.process_conv = nn.Sequential(
          nn.Conv2d(
              d_model, num_channels, kernel_size=1
          ),  # d_model, num_channels
          nn.LeakyReLU(negative_slope=0.01),
          ResidualBlock(num_channels, kernel_size=3),
      )
  
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 Dequantizer object from inherited base classes: nn with example: obj = Dequantizer(num_channels, d_model)