ea0e73d5531f1481848df657ccfcc8d98757ff20,texar/core/optimization.py,,get_optimizer,#Any#Any#Any#Any#,353

Before Change

        opt_argspec = utils.get_default_arg_values(optimizer_class.__init__)
        learning_rate = opt_argspec.get("learning_rate", None)

    grad_clip_fn = get_gradient_clip_fn(hparams["gradient_clip"])

    lr_decay_fn = get_learning_rate_decay_fn(hparams["learning_rate_decay"])
    if lr_decay_fn is not None:

After Change

                         global_step=global_step)
    else:
        lr = learning_rate
        tf.summary.scalar("learning_rate", lr)

    optimizer = optimizer_fn(learning_rate=lr)

    return optimizer

Instances