code: def get_variable_ddi(
      name,
      shape,
      value,
      init,
      initializer=None,
      dtype=tf.float32,
      regularizer=None,
      trainable=True,
  ):
      """Wrapper for data-dependent initialization."""
      kwargs = {"trainable": trainable}
      if initializer:
          kwargs["initializer"] = initializer
      if regularizer:
          kwargs["regularizer"] = regularizer
      w = tf.get_variable(name, shape, dtype, **kwargs)
      if isinstance(init, bool):
          if init:
              return assign(w, value)
          return w
      else:
          return tf.cond(init, lambda: assign(w, value), lambda: w)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import math
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models.transformer import transformer_decoder_layer
  import tensorflow.compat.v1
  import tensorflow_probability
label_desc: Use image node_id 17 for calling a global function with example usage: get_variable_ddi(name, shape, value, init, initializer, dtype, regularizer, trainable) and returns: w, tf, assign