code: def get_weight(shape, gain=np.sqrt(2), use_wscale=False, lrmul=1):
      fan_in = np.prod(
          shape[:-1]
      )  # [kernel, kernel, fmaps_in, fmaps_out] or [in, out]
      he_std = gain / np.sqrt(fan_in)  # He init
  
      # Equalized learning rate and custom learning rate multiplier.
      if use_wscale:
          init_std = 1.0 / lrmul
          runtime_coef = he_std * lrmul
      else:
          init_std = he_std / lrmul
          runtime_coef = lrmul
  
      # Create variable.
      init = tf.initializers.random_normal(0, init_std)
      return (
          tf.get_variable("weight", shape=shape, initializer=init)
          * runtime_coef
      )
  
imports: import numpy
  import tensorflow
  import dnnlib
  import dnnlib.tflib
label_desc: Use image node_id 7 for calling a global function with example usage: get_weight(shape, gain, use_wscale, lrmul) and returns: unknown