docstr: Analysis transfrom from scale hyperprior (https://arxiv.org/abs/1802.01436)
  
  Encodes each image in a video independently.
docstr_tok: ["Analysis","transfrom","from","scale","hyperprior","(","https",":","\/\/arxiv.org\/abs\/1802.01436",")","Encodes","each","image","in","a","video","independently","."]
code: def __init__(
      self, network_channels: int = 128, compression_channels: int = 192
  ) -> None:
      """
      Analysis transfrom from scale hyperprior (https://arxiv.org/abs/1802.01436)
  
      Encodes each image in a video independently.
      """
      super().__init__()
      self._compression_channels = compression_channels
  
      self.transforms = nn.Sequential(
          make_conv(3, network_channels, kernel_size=5, stride=2),
          GDN(network_channels),
          make_conv(
              network_channels,
              network_channels,
              kernel_size=5,
              stride=2,
          ),
          GDN(network_channels),
          make_conv(
              network_channels,
              network_channels,
              kernel_size=5,
              stride=2,
          ),
          GDN(network_channels),
          make_conv(
              network_channels,
              compression_channels,
              kernel_size=5,
              stride=2,
          ),
      )
      self._num_down = 4
  
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 ConvGDNAnalysis object from inherited base classes: nn with example: obj = ConvGDNAnalysis(network_channels, compression_channels)