r""" parameters(recurse=True) -> Iterator[Parameter] Returns an iterator over module parameters. This is typically p

Returns the mean of the distribution.

mock key thorugh value.

r"""Returns a dictionary containing a whole state of the graph. States of modules/optimizers/lr schedulers in a graph are included.

Performs a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and

r"""Add an optimizer, an learning rate scheduler to the graph. To do training with nn.Graph, you should do 2 more things: 1. Add at

Common

r"""Make a deep copy of value, casting all tensors to device or placement of param.

r""" cuda(device=None) Moves all model parameters and buffers to the GPU. This also makes associated parameters and