tensorflow_probability/python/optimizer/linesearch/hager_zhang.py
def _print(pass_through_tensor, values):
  """Wrapper for tf.Print which supports lists and namedtuples for printing."""
  flat_values = []
  for value in values:
    # Checks if it is a namedtuple.
    if hasattr(value, '_fields'):
      for field in value._fields:
        flat_values.extend([field, _to_str(getattr(value, field))])
      continue
    if isinstance(value, (list, tuple)):
      for v in value:
        flat_values.append(_to_str(v))
      continue
    flat_values.append(_to_str(value))
  return tf.compat.v1.Print(pass_through_tensor, flat_values)
def _print(pass_through_tensor, values):
  """Wrapper for tf.Print which supports lists and namedtuples for printing."""
  flat_values = []
  for value in values:
    # Checks if it is a namedtuple.
    if hasattr(value, '_fields'):
      for field in value._fields:
        flat_values.extend([field, _to_str(getattr(value, field))])
      continue
    if isinstance(value, (list, tuple)):
      for v in value:
        flat_values.append(_to_str(v))
      continue
    flat_values.append(_to_str(value))
  return tf.compat.v1.Print(pass_through_tensor, flat_values)
def _print ( pass_through_tensor , values ) : flat_values = [ ] for value in values : # Checks if it is a namedtuple. if hasattr ( value , '_fields' ) : for field in value . _fields : flat_values . extend ( [ field , _to_str ( getattr ( value , field ) ) ] ) continue if isinstance ( value , ( list , tuple ) ) : for v in value : flat_values . append ( _to_str ( v ) ) continue flat_values . append ( _to_str ( value ) ) return tf . compat . v1 . Print ( pass_through_tensor , flat_values )
Wrapper for tf.Print which supports lists and namedtuples for printing.
Wrapper for tf . Print which supports lists and namedtuples for printing .