How to use custom_equals method in pandera

Best Python code snippet using pandera_python

base.py

Source:base.py

1from pyrl.utils import collate, create_random_space, torchify2import torch3NUM_SAMPLES = 1004def custom_equals(a, b):5    if isinstance(a, dict):6        assert isinstance(b, dict) and b.keys() == a.keys()7        for k, v in a.items():8            custom_equals(v, b[k])9    else:10        print("ISCLOSE:", torch.isclose(a.float(), b.float()))11        assert (12            isinstance(b, torch.Tensor)13            and b.dim() == 214            and torch.all(torch.isclose(a.float(), b.float()))15        )16def make_test_multi_space(model, size=20):17    def test_multi_space():18        for _ in range(NUM_SAMPLES):19            space = create_random_space()20            m = model(space)21            samples = []22            for _ in range(size):23                samples.append({"obs": space.sample()})24            sample = collate([torchify(s) for s in samples])["obs"]25            print("=" * 50)26            print("SPACE:", space)27            print("SAMPLE:", sample)28            forward = m.forward(sample)29            print("FORWARD:", forward)30            custom_equals(forward, sample)31    return test_multi_space32def make_test_single_space(model):...

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