Back to Home11/2/2025, 5:23:40 PM
Untitled
You can do that in python using https://github.com/patrick-kidger/torchtyping
looks like this:
def batch_outer_product(x: TensorType["batch", "x_channels"],
y: TensorType["batch", "y_channels"]
) -> TensorType["batch", "x_channels", "y_channels"]:
return x.unsqueeze(-1) * y.unsqueeze(-2)
There's also https://github.com/thomasahle/tensorgrad which uses sympy for "axis" dimension variables: b, x, y = sp.symbols("b x y")
X = tg.Variable("X", b, x)
Y = tg.Variable("Y", b, y)
W = tg.Variable("W", x, y)
XWmY = X @ W - YDiscussion Activity
Light discussionFirst comment
14h
Peak period
2
Day 1
Avg / period
2
Comment distribution2 data points
Based on 2 loaded comments
Key moments
- 01Story posted
11/2/2025, 5:23:40 PM
16d ago
Step 01 - 02First comment
11/3/2025, 7:00:29 AM
14h after posting
Step 02 - 03Peak activity
2 comments in Day 1
Hottest window of the conversation
Step 03 - 04Latest activity
11/3/2025, 11:33:39 AM
16d ago
Step 04
Generating AI Summary...
Analyzing up to 500 comments to identify key contributors and discussion patterns
Discussion (0 comments)
Discussion hasn't started yet.
ID: 45791871Type: commentLast synced: 11/17/2025, 7:49:18 AM
Want the full context?
Jump to the original sources
Read the primary article or dive into the live Hacker News thread when you're ready.