utils

Clip the global gradient norm of `parameters` in place.

Clamp every gradient element to $[-\text{clip\_value}, \text{clip\_value}]$ in place.

Compute the global gradient $\ell_p$ norm without clipping.

Flatten an iterable of parameters into a single 1-D `Tensor`.

Copy a flat 1-D tensor back into the supplied parameters in place.

Reverse `weight_norm` and restore a plain leaf parameter.

Reverse `spectral_norm` and restore a plain leaf parameter.

Pack a padded $(T, B, *)$ batch into a `PackedSequence`.

One-shot pack-from-list — equivalent to `pad_sequence` then `pack_padded_sequence`.

Inverse of `pack_padded_sequence` — produce a padded tensor.

Stack a list of variable-length tensors into a padded batch.

Copy all parameter / buffer values from `source` into `dest`.

Fold a BatchNorm layer into the preceding Conv weights (inference-only).

Low-level form of `fuse_conv_bn_eval` operating on raw tensors.

Fold a BatchNorm1d into the preceding Linear weights (inference-only).

Low-level form of `fuse_linear_bn_eval` operating on raw tensors.