code: def moments(x, axes, keepdims=False, synchronized=False):
      if synchronized:
          raise NotImplementedError(
              "Argument synchronized=True is not supported with JAX."
          )
      # The dynamic range of float16 is too limited for statistics. As a
      # workaround, we simply perform the operations on float32 and convert back
      # to float16
      need_cast = False
      ori_dtype = standardize_dtype(x.dtype)
      if ori_dtype == "float16":
          need_cast = True
          x = cast(x, "float32")
  
      mean = jnp.mean(x, axes, keepdims=True)
  
      # The variance is computed using $Var = E[|x|^2] - |E[x]|^2$, It is faster
      # but less numerically stable.
      # Note: stop_gradient does not change the gradient to the mean, because that
      # gradient is zero.
      # The substraction operation does not guarantee a non-negative
      # result given float precision, so we clamp it to 0.
      variance = jnp.maximum(
          jnp.mean(jnp.square(x), axis=axes, keepdims=True)
          - jnp.square(jax.lax.stop_gradient(mean)),
          0.0,
      )
  
      if not keepdims:
          mean = jnp.squeeze(mean, axes)
          variance = jnp.squeeze(variance, axes)
      if need_cast:
          # avoid overflow and underflow when casting from float16 to float32
          mean = jnp.clip(
              mean,
              jnp.finfo(jnp.float16).min,
              jnp.finfo(jnp.float16).max,
          )
          variance = jnp.clip(
              variance,
              jnp.finfo(jnp.float16).min,
              jnp.finfo(jnp.float16).max,
          )
          mean = cast(mean, ori_dtype)
          variance = cast(variance, ori_dtype)
      return mean, variance
  
imports: import jax
  import jax.numpy
  import numpy
  from jax import lax
  from jax import nn
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_jax
  from keras.backend.config import epsilon
  from keras.backend.jax.core import cast
  from keras.backend.jax.core import convert_to_tensor
label_desc: Use image node_id 31 for calling a global function with example usage: moments(x, axes, keepdims, synchronized) and returns: mean,  variance