class_docstr: Convolutional DQN-inspired encoder for Atari games.
class_docstr_tok: ["Convolutional","DQN-inspired","encoder","for","Atari","games","."]
code: def __call__(self, x):
      initializer = nn.initializers.xavier_uniform()
      for channel, ks_size, stride_size in zip(
          self.conv_channels, self.conv_kernels, self.conv_strides
      ):
          x = nn.Conv(
              features=channel * self.nn_scale,
              kernel_size=(ks_size, ks_size),
              strides=(stride_size, stride_size),
              kernel_init=initializer,
          )(x)
          x = nn.relu(x)
      return x
  
imports: import collections
  from typing import Tuple
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from flax import linen
  import gin
  import jax
  import jax.numpy
label_desc: Use image node_id 1 for calling the CNNEncoder obj's underlying member method code with example usage: obj.__call__(x) and returns: x