It should yield the same results as seen here:
x = torch.randn(2, 3, 24, 24)
conv = nn.Conv2d(3, 6, 3)
pad = nn.ReflectionPad2d(1)

out = pad(x)
out = conv(out)

conv_pad = nn.Conv2d(3, 6, 3, 1, 1, padding_mode='reflect')
conv_pad.load_state_dict(conv.state_dict())

out_pad = conv_pad(x)

print((out - out_pad).abs().max())
> tensor(0., grad_fn=<MaxBackward1>)