code: def batch_norm(
      scope: Scope,
      x,
      use_running_average=False,
      axis=-1,
      momentum=0.99,
      epsilon=1e-5,
      dtype=jnp.float32,
      bias=True,
      scale=True,
      bias_init=initializers.zeros_init(),
      scale_init=initializers.ones_init(),
      axis_name=None,
      axis_index_groups=None,
      kind="batch_stats",
  ):
      x = jnp.asarray(x, jnp.float32)
      axis = axis if isinstance(axis, tuple) else (axis,)
      axis = _absolute_dims(x.ndim, axis)
      redux = tuple(i for i in range(x.ndim) if i not in axis)
  
      def pmean(x):
          m = jnp.mean(x, redux, keepdims=True)
          if axis_name is not None:
              m = lax.pmean(
                  m,
                  axis_name=axis_name,
                  axis_index_groups=axis_index_groups,
              )
          return m
  
      mean = pmean(x)
      squeeze_shape = jnp.squeeze(mean).shape
      mean2 = pmean(jnp.square(x))
      var = mean2 - jnp.square(mean)
  
      is_init = not scope.has_variable(kind, "mean")
      ra_mean = scope.variable(kind, "mean", jnp.zeros, squeeze_shape)
      ra_var = scope.variable(kind, "var", jnp.ones, squeeze_shape)
  
      if use_running_average:
          # if ra_mean is not None:
          #   raise ValueError('batch_stats should be provided if use_running_averages=True')
          mean = jnp.reshape(ra_mean.value, mean.shape)
          var = jnp.reshape(ra_var.value, var.shape)
      else:
          if not is_init:
              beta = 1.0 - momentum
              ra_mean.value += beta * (
                  jnp.squeeze(mean) - ra_mean.value
              )
              ra_var.value += beta * (jnp.squeeze(var) - ra_var.value)
      y = x - mean
      mul = lax.rsqrt(var + epsilon)
      if scale:
          mul = mul * scope.param(
              "scale", scale_init, squeeze_shape
          ).reshape(mean.shape)
      y = y * mul
      if bias:
          y = y + scope.param("bias", bias_init, squeeze_shape).reshape(
              mean.shape
          )
      return jnp.asarray(y, dtype)
  
imports: import jax.numpy
  from jax import lax
  from flax.core import Scope
  from flax.linen import initializers
label_desc: Use image node_id 2 for calling a global function with example usage: batch_norm(scope, x, use_running_average, axis, momentum, epsilon, dtype, bias, scale, bias_init, scale_init, axis_name, axis_index_groups, kind) and returns: jnp, m