code: def __call__(
      self, x, *, train: bool, rngs: tp.Optional[nnx.Rngs] = None
  ) -> jax.Array:
      if self.is_initializing and not hasattr(self, "scale"):
          if rngs is None:
              raise ValueError(
                  "rngs must be provided to initialize module"
              )
          self.init_variables(x.shape[-1], rngs)
  
      if train:
          axis = tuple(range(x.ndim - 1))
          mean = jax.numpy.mean(x, axis=axis)
          var = jax.numpy.var(x, axis=axis)
          # ema update
          self.mean = self.mu * self.mean + (1 - self.mu) * mean
          self.var = self.mu * self.var + (1 - self.mu) * var
      else:
          mean, var = self.mean, self.var
  
      scale, bias = self.scale, self.bias
      x = (x - mean) / jax.numpy.sqrt(var + 1e-5) * scale + bias
      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 6 for calling the BatchNorm obj's underlying member method code with example usage: obj.__call__(x) and returns: x