tf.compat.v1.flags.mark_flags_as

Ensures that flag is not None during program execution.

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Compat aliases for migration

tf.compat.v1.app.flags.mark_flag_as_required

tf.compat.v1.flags.mark_flag_as_required(
    flag_name, flag_values=_flagvalues.FLAGS
)

Registers a flag validator, which will follow usual validator rules. Important note: validator will pass for any non-None value, such as False, 0 (zero), '' (empty string) and so on.

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_flag_as_required('your_flag_name') app.run()

Args
`flag_name`	str, name of the flag
`flag_values`	flags.FlagValues, optional FlagValues instance where the flag is defined.

Raises
`AttributeError`	Raised when flag_name is not registered as a valid flag name.

TensorFlow

tf.compat / v1 / v1.flags.mark_flags_as_required

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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_required

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Args

Raises