In the inference stage, why "standardize input function" use input_means(models) instead of means of input_tensor(input)?

[lzyplayer]

In the inference stage, it seems that the mean and standard deviation used for standardization are extracted from the model instead of calculating the input input_tensor, so the normalized result is not a distribution with a mean of 0 and a standard deviation of 1. , which is inconsistent with the description in the first paragraph of the appendix of the paper. Did I miss something? /model/BaseModel.py line 37

    # standarize input
    def standardizeInput(self, input_tensor):
        return ((input_tensor - self.input_means.type_as(input_tensor)) / self.input_scales.type_as(input_tensor))

    # standarize output
    def standardizeOutput(self, output_tensor):
        return ((output_tensor - self.output_means.type_as(output_tensor)) / self.output_scales.type_as(output_tensor))

    # Add scale and means to output
    def destandardizeInput(self, input_tensor):
        return (input_tensor * self.input_scales.type_as(input_tensor) + self.input_means.type_as(input_tensor))

    # Add scale and means to output
    def destandardizeOutput(self, predictions):
        return (predictions * self.output_scales.type_as(predictions) + self.output_means.type_as(predictions))

[lzyplayer]

was my mistake, normalizing only on the test set doesn't make sense