code: def _make_masked_initializer(mask, initializer):
      """Returns a masked version of the given initializer."""
      initializer = tf_keras.initializers.get(initializer)
  
      def masked_initializer(shape, dtype=None, partition_info=None):
          # If no `partition_info` is given, then don't pass it to `initializer`, as
          # `initializer` may be a `tf_keras.initializers.Initializer` (which don't
          # accept a `partition_info` argument).
          if partition_info is None:
              x = initializer(shape, dtype)
          else:
              x = initializer(shape, dtype, partition_info)
          return tf.cast(mask, x.dtype) * x
  
      return masked_initializer
  
imports: import numpy
  import six
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math.numeric import clip_by_value_preserve_gradient
  from tensorflow.python.util import deprecation
label_desc: Use image node_id 10 for calling a global function with example usage: _make_masked_initializer(mask, initializer) and returns: masked_initializer, unknown