code: def __call__(self, x: jax.Array, *, rngs: nnx.Rngs) -> jax.Array:
      x = x.reshape((x.shape[0], -1))  # flatten
      x = self.linear1(x)
      x = jax.nn.relu(x)
  
      mean = self.linear_mean(x)
      std = jnp.exp(self.linear_std(x))
  
      self.kl_loss = Loss(
          jnp.mean(
              0.5
              * jnp.mean(
                  -jnp.log(std**2) - 1.0 + std**2 + mean**2,
                  axis=-1,
              )
          )
      )
      key = rngs.noise()
      z = mean + std * jax.random.normal(key, mean.shape)
      return z
  
imports: import typing
  from functools import partial
  import jax
  import jax.numpy
  import matplotlib.pyplot
  import numpy
  import optax
  from datasets import load_dataset
  from flax.experimental import nnx
label_desc: Use image node_id 2 for calling the Encoder obj's underlying member method code with example usage: obj.__call__(x) and returns: z