How does stochastic gradient descent undo the normalization done by the batch normalization?

I want to understand the handshake between SGD (or mini-batch GD) and batch normalization. Below, an explanation quoted from this Medium article. However, I am confused about the denormalization by the SGD.

SGD ( Stochastic gradient descent) undoes this normalization if it’s a way for it to minimize the loss function.

Consequently, batch normalization adds two trainable parameters to each layer, so the normalized output is multiplied by a “standard deviation” parameter (gamma) and add a “mean” parameter (beta). In other words, batch normalization lets SGD do the denormalization by changing only these two weights for each activation, instead of losing the stability of the network by changing all the weights.

• This Medium article! Mar 3 at 15:00

$$\text{BatchNorm}({\mathcal{X}}_{b}) = \gamma \cdot \frac{(\mathcal{X}_{b} - \text{mean}(\mathcal{X}_{b}))}{\text{std}(\mathcal{X}_{b})} + \beta$$