code: def _compute_stats(x: Array, axes: Axes):
      """Computes mean and variance statistics.
  
      This implementation takes care of a few important details:
      - Computes in float32 precision for half precision inputs
      -  mean and variance is computable in a single XLA fusion,
        by using Var = E[|x|^2] - |E[x]|^2 instead of Var = E[|x - E[x]|^2]).
      - Clips negative variances to zero which can happen due to
        roundoff errors. This avoids downstream NaNs.
      - Supports averaging across a parallel axis and subgroups of a parallel axis
        with a single `lax.pmean` call to avoid latency.
      """
      # promote x to at least float32, this avoids half precision computation
      # but preserves double or complex floating points
      x = jnp.asarray(
          x, jnp.promote_types(jnp.float32, jnp.result_type(x))
      )
      mean = jnp.mean(x, axes)
      mean2 = jnp.mean(_abs_sq(x), axes)
      # mean2 - _abs_sq(mean) is not guaranteed to be non-negative due
      # to floating point round-off errors.
      var = jnp.maximum(0.0, mean2 - _abs_sq(mean))
      return mean, var
  
imports: import dataclasses
  from typing import Any, Callable, Iterable, Optional, Tuple, Union
  import jax.numpy
  from jax import lax
  from flax import linen
  from flax.linen import initializers
  from flax.linen.linear import DotGeneralT, PrecisionLike
  from flax.linen.partitioning import param_with_axes, with_sharding_constraint
label_desc: Use image node_id 3 for calling a global function with example usage: _compute_stats(x, axes) and returns: mean,  var