code: def __call__(
      self, x: jax.Array, *, train: bool, rngs: nnx.Rngs
  ) -> jax.Array:
      if train:
          mask = random.bernoulli(
              rngs.dropout(), (1 - self.rate), x.shape
          )
          x = x * mask / (1 - self.rate)
      return x
  
imports: import typing
  import jax
  import jax.numpy
  from jax import random
  from flax.experimental import nnx
label_desc: Use image node_id 2 for calling the Dropout obj's underlying member method code with example usage: obj.__call__(x) and returns: x