def METHOD_NAME(self, N, C, H, W, dtype, contig):
    # Input
    if N == 0:
        f_input = (torch.rand(C, H, W) - 0.5) * 256
    else:
        f_input = (torch.rand(N, C, H, W) - 0.5) * 256
    scale = 1.0
    zero_point = 0
    # Quantize the tensor
    self.q_input = torch.quantize_per_tensor(
        f_input, scale=scale, zero_point=zero_point, dtype=dtype
    )
    if not contig:
        # Permute into NHWC and back to make it non-contiguous
        if N == 0:
            self.q_input = self.q_input.permute(1, 2, 0).contiguous()
            self.q_input = self.q_input.permute(2, 0, 1)
        else:
            self.q_input = self.q_input.permute(0, 2, 3, 1).contiguous()
            self.q_input = self.q_input.permute(0, 3, 1, 2)
    self.inputs = {"q_input": self.q_input}