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# Required Libraries
import unittest
import torch
from models.helpers import positional_encoding
class TestPositionalEncoding(unittest.TestCase):
def test_positional_encoding(self):
# Test with d_model=128, length=10
d_model = 128
length = 10
result = positional_encoding(d_model, length)
# Assert that output is a torch.Tensor
self.assertIsInstance(result, torch.Tensor)
# Assert the output tensor shape is correct
# The extra dimension from unsqueeze is considered
expected_shape = (1, length, d_model)
self.assertEqual(result.shape, expected_shape)
# Assert that values lie in the range [-1, 1]
self.assertTrue(torch.all((result >= -1) & (result <= 1)))
# Run tests
if __name__ == "__main__":
unittest.main()
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