tf.compat.v1.flags.mark_flags_as_mutual

Ensures that flags are not None during program execution.

View aliases

Compat aliases for migration

tf.compat.v1.app.flags.mark_flags_as_required

tf.compat.v1.flags.mark_flags_as_required(
    flag_names, flag_values=_flagvalues.FLAGS
)

If your module might be imported by others, and you only wish to make the flag required when the module is directly executed, call this method like this:

if name == 'main': flags.mark_flags_as_required(['flag1', 'flag2', 'flag3']) app.run()

Args
`flag_names`	Sequence[str], names of the flags.
`flag_values`	flags.FlagValues, optional FlagValues instance where the flags are defined.

Raises
`AttributeError`	If any of flag name has not already been defined as a flag.

TensorFlow

tf.compat / v1 / v1.flags.mark_flags_as_mutual_exclusive

View aliases

Args

Raises

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.flags.mark_flags_as_mutual_exclusive

View aliases

Args

Raises