tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.clone(name=None, **dist_args)

tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See

tf.contrib.distributions.TransformedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters

tf.contrib.graph_editor.copy_with_input_replacements(sgv, replacement_ts, dst_graph=None, dst_scope='', src_scope='', reuse_dst_scope=False)

tf.contrib.distributions.Binomial.prob(value, name='prob') Probability density/mass function (depending on is_continuous)

tf.contrib.distributions.StudentT.prob(value, name='prob') Probability density/mass function (depending on is_continuous)

tf.contrib.learn.monitors.LoggingTrainable.step_begin(step) Overrides BaseMonitor.step_begin. When

tf.contrib.distributions.LaplaceWithSoftplusScale.validate_args Python boolean indicated possibly expensive checks are enabled

tf.contrib.learn.monitors.RunHookAdapterForMonitors.after_run(run_context, run_values)

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.distribution