code: def group_norm(
      scope,
      x,
      num_groups=32,
      group_size=None,
      epsilon=1e-6,
      dtype=jnp.float32,
      bias=True,
      scale=True,
      bias_init=initializers.zeros_init(),
      scale_init=initializers.ones_init(),
  ):
      """Applies group normalization to the input (arxiv.org/abs/1803.08494).
      This op is similar to batch normalization, but statistics are shared across
      equally-sized groups of channels and not shared across batch dimension.
      Thus, group normalization does not depend on the batch composition and does
      not require maintaining internal state for storing statistics.
      The user should either specify the total number of channel groups or the
      number of channels per group.
      Args:
        x: the input of shape N...C, where N is a batch dimension and C is a
          channels dimensions. `...` represents an arbitrary number of extra
          dimensions that are used to accumulate statistics over.
        num_groups: the total number of channel groups. The default value of 32 is
          proposed by the original group normalization paper.
        group_size: the number of channels in a group.
        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).
      """
      x = jnp.asarray(x, jnp.float32)
      if (num_groups is None and group_size is None) or (
          num_groups is not None and group_size is not None
      ):
          raise ValueError(
              "Either `num_groups` or `group_size` should be "
              "specified, but not both of them."
          )
  
      if group_size is not None:
          channels = x.shape[-1]
          if channels % group_size != 0:
              raise ValueError(
                  "Number of channels ({}) is not multiple of the "
                  "group size ({}).".format(channels, group_size)
              )
          num_groups = channels // group_size
  
      input_shape = x.shape
      group_shape = x.shape[:-1] + (
          num_groups,
          x.shape[-1] // num_groups,
      )
  
      x = x.reshape(group_shape)
  
      reduction_axis = list(range(1, x.ndim - 2)) + [x.ndim - 1]
  
      mean = jnp.mean(x, axis=reduction_axis, keepdims=True)
      mean_of_squares = jnp.mean(
          jnp.square(x), axis=reduction_axis, keepdims=True
      )
      var = mean_of_squares - jnp.square(mean)
  
      x = (x - mean) * lax.rsqrt(var + epsilon)
  
      x = x.reshape(input_shape)
  
      feature_shape = tuple(
          [1 for d in input_shape[:-1]] + [input_shape[-1]]
      )
      if scale:
          x = x * scope.param("scale", scale_init, feature_shape)
      if bias:
          x = x + scope.param("bias", bias_init, feature_shape)
  
      return x.astype(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 4 for calling a global function with example usage: group_norm(scope, x, num_groups, group_size, epsilon, dtype, bias, scale, bias_init, scale_init) and returns: x