If pred.shape is (a, b, c), pred[:, :, 0].shape is (a, b), while pred[:, :, 0:1].shape is (a, b, 1). To keep the dimensions consistent in the concatenation, I think that pred[:, :, 0:1] should be used.
The following code converts the Tensor to a Python float.
anchors = torch.FloatTensor(anchors)

As a result, the variable “anchors” will be treated as a constant when you use the ONNX model. If this is not your intention, you need to avoid this conversion, and always use Torch Tensor in computation.