Open the context
Choose the project folder and name the files or behavior involved.
Workflows
Coding workflows · Tensor broadcast shapes
03
A concrete starting point
Infer a shared tensor shape by comparing trailing dimensions and expanding unit axes. Check scalar shapes and zero-sized axes with unit dimensions, rejecting incompatible, negative or fractional dimensions.
Define the change
Describe the input, expected output and boundaries that must stay intact.
Inspect the work
Review the proposed edits and any commands before relying on their result.
Check the outcome
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
Tensor broadcast shapes
A concrete starting point
from itertools import zip_longest
def broadcast_shape(a, b):
a, b = tuple(a), tuple(b)
if any(type(d) is not int or d < 0 for d in a + b):
raise ValueError("dimension.invalid")
result = []
for left, right in zip_longest(reversed(a), reversed(b), fillvalue=1):
if left == right or right == 1:
result.append(left)
elif left == 1:
result.append(right)
else:
raise ValueError("shape.incompatible")
return tuple(reversed(result))The example is a starting point for a real project, not a claim that generated code is already tested. Adjust paths, dependencies and expected behavior to match your application.