docstr: Synthesis transfrom from scale hyperprior (https://arxiv.org/abs/1802.01436)
  
  Decodes each image in a video independently
docstr_tok: ["Synthesis","transfrom","from","scale","hyperprior","(","https",":","\/\/arxiv.org\/abs\/1802.01436",")","Decodes","each","image","in","a","video","independently"]
code: def __init__(
      self, network_channels: int = 128, compression_channels: int = 192
  ) -> None:
      """
      Synthesis transfrom from scale hyperprior (https://arxiv.org/abs/1802.01436)
  
      Decodes each image in a video independently
      """
  
      super().__init__()
      self._compression_channels = 192
  
      self.transforms = nn.Sequential(
          make_deconv(
              compression_channels,
              network_channels,
              kernel_size=5,
              stride=2,
          ),
          GDN(network_channels, inverse=True),
          make_deconv(
              network_channels,
              network_channels,
              kernel_size=5,
              stride=2,
          ),
          GDN(network_channels, inverse=True),
          make_deconv(
              network_channels,
              network_channels,
              kernel_size=5,
              stride=2,
          ),
          GDN(network_channels, inverse=True),
          make_deconv(network_channels, 3, kernel_size=5, stride=2),
      )
  
imports: from typing import List, Optional
  import torch
  import torch.nn
  from compressai.layers import GDN, AttentionBlock
  from neural.layers_utils import make_conv, make_deconv
  from torch import Tensor
label_desc: Use image node_id 1 to create a new ConvGDNSynthesis object from inherited base classes: nn with example: obj = ConvGDNSynthesis(network_channels, compression_channels)