The existing assertion asserts that the effective ranks (after removing leading singleton axes) of the input tensor and output tensor are equal. This assertion fails in two tests:

[  FAILED  ] Test_ONNX_layers.CalculatePads/0, where GetParam() = CUDA/CUDA
[  FAILED  ] Test_ONNX_layers.CalculatePads/1, where GetParam() = CUDA/CUDA_FP16

The effective rank of the input may be smaller than the effective rank of the output but the converse is never true. For example,

input: [1, 1, 1, 3]; effective rank = 1
output: [1, 1, 3, 3]; effective rank = 2

There is padding for axis 2 which is a singleton axis in the input tensor.

The correct assertion is to check that the input effective rank is less than or equal to the output effective rank. This PR fixes the assertion and the tests pass.

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