Get a pre-defined beta schedule for the given name. The beta schedule library consists of beta schedules which remain similar in the lim

r""" Args: key: the key of the object state: the state of the object

r""" Loops of Learning Args: criterion: a Criterion instance train_dataset: the training dataset batch_size: the batch siz

r""" Calculate -log p_{t_init} (x) of the score ODE See `Score-Based Generative Modeling through Stochastic Differential Equations` Here w

r""" Loops of Learning Args: criterion: a Criterion instance train_dataset: the training dataset batch_size: the batch siz

Unflatten the master parameters to look like model_params.

Fused `upsample_2d()` followed by `tf.nn.conv2d()`. Padding is performed only once at the beginning, not between the operations. The f

Ported from JAX.

Args: index (int): Index Returns: tuple: Tuple (image, target) where target is the index of the target categ

r""" Record the states of training Args: it: iteration best_val_loss: the best validation loss models_stat

r""" Args: period: the period to report statistics

r""" Evaluate models

r""" Evaluate DPM with discrete timesteps Args: wrapper: an object of Wrapper options: a dict, evaluation function nam

r""" Evaluate DPM with continuous timesteps (i.e., SDE) Args: wrapper: an object of Wrapper options: a dict, evaluatio

r""" Criterion does 1. calculating objectives 2. calculating gradients 3. updating parameters Args: