class_docstr: MuZero-style transition model cell, used for SPR.
  
  Attributes:
    num_actions: how many actions are possible (shape of one-hot vector)
    latent_dim: number of channels in representation.
    renormalize: whether or not to apply renormalization.
code: def __call__(self, x, action, eval_mode=False, key=None):
      sizes = [self.latent_dim, self.latent_dim]
      kernel_sizes = [3, 3]
      stride_sizes = [1, 1]
  
      action_onehot = jax.nn.one_hot(action, self.num_actions)
      action_onehot = jax.lax.broadcast(
          action_onehot, (x.shape[-3], x.shape[-2])
      )
      x = jnp.concatenate([x, action_onehot], -1)
      for layer in range(1):
          x = nn.Conv(
              features=sizes[layer],
              kernel_size=(kernel_sizes[layer], kernel_sizes[layer]),
              strides=(stride_sizes[layer], stride_sizes[layer]),
              kernel_init=nn.initializers.xavier_uniform(),
          )(x)
          x = nn.relu(x)
      x = nn.Conv(
          features=sizes[-1],
          kernel_size=(kernel_sizes[-1], kernel_sizes[-1]),
          strides=(stride_sizes[-1], stride_sizes[-1]),
          kernel_init=nn.initializers.xavier_uniform(),
      )(x)
      x = nn.relu(x)
  
      if self.renormalize:
          x = renormalize(x)
  
      return x, x
  
imports: import collections
  import functools
  import time
  from typing import Any, Callable, Optional, Tuple
  from dopamine.jax.networks import preprocess_atari_inputs
  from flax import linen
  import gin
  import jax
  from jax import lax
  from jax import random
  from jax.nn import initializers
  import jax.numpy
  import numpy
label_desc: Use image node_id 2 for calling the ConvTMCell obj's underlying member method code with example usage: obj.__call__(x, action, eval_mode, key) and returns: x,  x