def __init__(self,<EOL>scale,<EOL>validate_args=False,<EOL>allow_nan_stats=True,<EOL>name="<STR_LIT>"):
parameters = dict(locals())<EOL>with tf.name_scope(name) as name:<EOL><INDENT>scale = tf.convert_to_tensor(<EOL>value=scale,<EOL>name="<STR_LIT>",<EOL>dtype=dtype_util.common_dtype([scale], preferred_dtype=tf.float32))<EOL>with tf.control_dependencies(<EOL>[assert_util.assert_positive(scale)] if validate_args else []):<EOL><INDENT>self._scale = tf.identity(scale, name="<STR_LIT>")<EOL><DEDENT><DEDENT>super(HalfNormal, self).__init__(<EOL>dtype=self._scale.dtype,<EOL>reparameterization_type=reparameterization.FULLY_REPARAMETERIZED,<EOL>validate_args=validate_args,<EOL>allow_nan_stats=allow_nan_stats,<EOL>parameters=parameters,<EOL>graph_parents=[self._scale],<EOL>name=name)<EOL>
Construct HalfNormals with scale `scale`.

        Args:
          scale: Floating point tensor; the scales of the distribution(s).
            Must contain only positive values.
          validate_args: Python `bool`, default `False`. When `True` distribution
            parameters are checked for validity despite possibly degrading runtime
            performance. When `False` invalid inputs may silently render incorrect
            outputs.
          allow_nan_stats: Python `bool`, default `True`. When `True`,
            statistics (e.g., mean, mode, variance) use the value "`NaN`" to
            indicate the result is undefined. When `False`, an exception is raised
            if one or more of the statistic's batch members are undefined.
          name: Python `str` name prefixed to Ops created by this class.