Create a linear module.

Appends dimensions to the end of a tensor until it has target_dims dimensions.

Create a 1D, 2D, or 3D convolution module.

Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas

Make a standard normalization layer.

Create sinusoidal timestep embeddings. :param timesteps: A 1-D Tensor of N indices, one per batch element. These may be fractional. :par

Initializes Unit3D module.

Estimate a covariance matrix given data. Covariance indicates the level to which two variables vary together. If we examine N-dimensiona

Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with ``load_state_dict()`` and used for training

Restore the parameters stored with the 'store' method. Useful to validate the model with EMA parameters without affecting the

Save the current parameters for restoring later. Args: parameters: Iterable of 'torch.nn.Parameter'; the parameters to be te

Zero out the parameters of a module and return it.

Zero out the parameters of a module and return it.