tensorflow_probability/python/bijectors/masked_autoregressive.py
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.compat.v2.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
def _make_masked_initializer(mask, 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.compat.v2.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
def _make_masked_initializer ( mask , 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.compat.v2.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
Returns a masked version of the given initializer.
Returns a masked version of the given initializer .