code: def minibatch_stddev_layer(x, group_size=4, num_new_features=1):
      with tf.variable_scope("MinibatchStddev"):
          group_size = tf.minimum(
              group_size, tf.shape(x)[0]
          )  # Minibatch must be divisible by (or smaller than) group_size.
          s = x.shape  # [NCHW]  Input shape.
          y = tf.reshape(
              x,
              [
                  group_size,
                  -1,
                  num_new_features,
                  s[1] // num_new_features,
                  s[2],
                  s[3],
              ],
          )  # [GMncHW] Split minibatch into M groups of size G. Split channels into n channel groups c.
          y = tf.cast(y, tf.float32)  # [GMncHW] Cast to FP32.
          y -= tf.reduce_mean(
              y, axis=0, keepdims=True
          )  # [GMncHW] Subtract mean over group.
          y = tf.reduce_mean(
              tf.square(y), axis=0
          )  # [MncHW]  Calc variance over group.
          y = tf.sqrt(y + 1e-8)  # [MncHW]  Calc stddev over group.
          y = tf.reduce_mean(
              y, axis=[2, 3, 4], keepdims=True
          )  # [Mn111]  Take average over fmaps and pixels.
          y = tf.reduce_mean(
              y, axis=[2]
          )  # [Mn11] Split channels into c channel groups
          y = tf.cast(
              y, x.dtype
          )  # [Mn11]  Cast back to original data type.
          y = tf.tile(
              y, [group_size, 1, s[2], s[3]]
          )  # [NnHW]  Replicate over group and pixels.
          return tf.concat([x, y], axis=1)
  
imports: import numpy
  import tensorflow
  import dnnlib
  import dnnlib.tflib
label_desc: Use image node_id 18 for calling a global function with example usage: minibatch_stddev_layer(x, group_size, num_new_features) and returns: tf