Traceback (most recent call last):
  File "/home/franz/iNNfer/minimal.py", line 52, in <module>
    upscale_image(input_image_path, output_image_path)
  File "/home/franz/iNNfer/minimal.py", line 39, in upscale_image
    upscaled_image = model(image_tensor).clamp(0.0, 1.0)
  File "/home/franz/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/franz/iNNfer/model_definition.py", line 56, in forward
    out = rrdb_block(out)
  File "/home/franz/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/franz/iNNfer/model_definition.py", line 20, in forward
    out = layer(out)
  File "/home/franz/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/franz/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 457, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/home/franz/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 453, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [32, 96, 3, 3], expected input[1, 32, 1536, 1024] to have 96 channels, but got 32 channels instead