Repeat action, and sum reward

In the backward pass we receive a Tensor containing the gradient of the loss with respect to the output, and we need to compute the g

Train a model over multiple epochs and return elapsed time and average loss. Running multiple epochs (10) with a small dataset ensures many epoch

Context manager to capture and save PyTorch CUDA memory snapshots. This captures all GPU memory allocations during the context and saves them

Create a base ``env``.

Apply transforms to the ``env`` (such as reward scaling and state normalization).

Import machinery for ``cpp_lib.so`` can get repetitive to look at.

A stable 60-bit track UUID derived from its identifying parts.

takes torch.benchmark objects and compares delta of second vs first.

Display image for Tensor.

Save a transition

Show image with landmarks

Capture the model into a CUDA graph with annotations enabled.

Convert a Tensor to numpy image.