I’m unsure how the kernel size is related to the input shape of the batchnorm layer.
However, if the input contains only a single value for each channel (as is the case here), you won’t be able to use batchnorm layers in training mode, since they need to calculate the stats from the input.
Since it’s impossible to calculate the var from a single sample (it would create NaNs) and also subtracting the mean from a single value would create a zero output, you could remove these layers from the model.