code: def layer_norm(
      scope: Scope,
      x,
      epsilon=1e-6,
      dtype=jnp.float32,
      bias=True,
      scale=True,
      bias_init=initializers.zeros_init(),
      scale_init=initializers.ones_init(),
  ):
      """Applies layer normalization on the input.
      It normalizes the activations of the layer for each given example in a
      batch independently, rather than across a batch like Batch Normalization.
      i.e. applies a transformation that maintains the mean activation within
      each example close to 0 and the activation standard deviation close to 1.
      Args:
        x: the inputs
        epsilon: A small float added to variance to avoid dividing by zero.
        dtype: the dtype of the computation (default: float32).
        bias:  If True, bias (beta) is added.
        scale: If True, multiply by scale (gamma). When the next layer is linear
          (also e.g. nn.relu), this can be disabled since the scaling will be done
          by the next layer.
        bias_init: Initializer for bias, by default, zero.
        scale_init: Initializer for scale, by default, one.
      Returns:
        Normalized inputs (the same shape as inputs).
      """
      features = x.shape[-1]
      mean = jnp.mean(x, axis=-1, keepdims=True)
      mean2 = jnp.mean(lax.square(x), axis=-1, keepdims=True)
      var = mean2 - lax.square(mean)
      mul = lax.rsqrt(var + epsilon)
      if scale:
          mul = mul * jnp.asarray(
              scope.param("scale", scale_init, (features,)), dtype
          )
      y = (x - mean) * mul
      if bias:
          y = y + jnp.asarray(
              scope.param("bias", bias_init, (features,)), dtype
          )
      return y
  
imports: import jax.numpy
  from jax import lax
  from flax.core import Scope
  from flax.linen import initializers
label_desc: Use image node_id 3 for calling a global function with example usage: layer_norm(scope, x, epsilon, dtype, bias, scale, bias_init, scale_init) and returns: y