when I push my answer, It will raise error:

📝 Setup
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  /usr/local/lib/python3.10/dist-packages/numpy/__init__.pyi:642: error: Positional-only parameters are only supported in Python 3.8 and greater
  Found 1 error in 1 file (errors prevented further checking)

I have never had such a problem on my local computer.
Why does Python 3.10 not support the 3.8 feature? How should I locate this error?
By the way, the isort for pre-commit should update to version 5.12.0, otherwise, an error will occur during submission. Please make the necessary changes.

In https://github.com/minitorch/Module-1/blob/master/tests/test_autodiff.py line 53, are we sure that this assert is correct:
assert variable_with_deriv.deriv == 5?

Shouldn't it be assert variable_with_deriv.deriv == 5 * 10?

As it will only return a single variable with derivative (as the constants are dropped from the chain_rule) and multiplication is commutative.

So sorry to bother you.

Thanks for your nice code and tutorial, I really benefited a lot after finish module-1. But some thing seems to be wrong with the Class LT. It does not define a backward function, but the method will be tested in task1_4. Am i correct? Or am I misunderstanding something? Thank you so much.

class LT(ScalarFunction):
    "Less-than function :math:`f(x) =` 1.0 if x is less than y else 0.0"

    @staticmethod
    def forward(ctx, a, b):
        # TODO: Implement for Task 1.2.
        raise NotImplementedError('Need to implement for Task 1.2')
        # TODO: Implement for Task 1.2.
        raise NotImplementedError('Need to implement for Task 1.2')
        # TODO: Implement for Task 1.4.
        raise NotImplementedError('Need to implement for Task 1.4')

For Task 1.2, I believe the intended code for scalar.Scalar.__sub__is return Add.apply(self, -b), which passes the tests. Interestingly, if return Add.apply(self, Neg.apply(b)) is used, the tests that involve subtracting constants fail. Excerpt of the failed test cases below:

===============================short test summary info==============================
FAILED tests/test_scalar.py::test_one_args[fn4] - AssertionError: Expected return typ <class 'float'> got <class 'int'>: -200
FAILED tests/test_scalar.py::test_one_args[fn5] - AssertionError: Expected return typ <class 'float'> got <class 'int'>: -200
FAILED tests/test_scalar.py::test_one_args[fn13] - AssertionError: Expected return typ <class 'float'> got <class 'int'>: -5     

I've slightly modified the error message in autodiff.FunctionBase to print the offending value in question, and they are all ints. The root cause is due to scalar.ScalarFunction.data_type being hard-coded as float. I assume data_type is fixed for a reason, and there probably isn't an easy fix to allow the alternate solution to pass the tests. Just thought y'all should know.

cp ../Module-0/operators.py ../Module-0/module.py minitorch/

should be replaced with:

cp ../Module-0/minitorch/operators.py ../Module-0/minitorch/module.py minitorch/

Initialize a queue with the final variable+derivative.
While the queue is not empty, pull a variable+derivative from the queue.
If the variable is a leaf then add its final derivative (_add_deriv) and loop to (1).
If the variables is not a leaf call .chain_rule on the last function that created it with derivative as 𝑑𝑜𝑢𝑡.
Loop through all the variables+derivative produced by the chain rule.
If the variable is in the queue (check .name), add to its current derivativ
Otherwise, add to the queue.

Actually, it's not necessary to Loop through all the variables+derivative produced by the chain rule. Even though we don't check for repetition, the chain rule and sum rule will automatically accumulate the deriv to the leaf node.