Are the training samples shuffled in minibatch gradient descent? [duplicate]

Lets say that I have 3,200 observations that I want to use for training a neural network model, and I want to set the batch size to 32. The number of minibatches used in every epoch for updating the weights is therefore 100.

How will my data be divided during minibatching? Assuming I have datapoints $$x_{1}, x_{2}, ... , x_{3200}$$ - will minibatch slice the data as it is, and use the same slices in every epoch? e.g. $$Epoch 1: minibatch1: [x_{1}, x_{2}, ... , x_{32}], minibatch2: [x_{33}, x_{34}, ... , x_{64}] \\ Epoch 2: minibatch1: [x_{1}, x_{2}, ... , x_{32}], minibatch2: [x_{33}, x_{34}, ... , x_{64}]\\ Epoch 40: minibatch1: [x_{1}, x_{2}, ... , x_{32}], minibatch2: [x_{33}, x_{34}, ... , x_{64}]$$

Or are the slices shuffled in every epoch, such that there are different combinations of observations in every minibatch in every training epoch? e.g. $$Epoch 1: minibatch1: [x_{67}, x_{2891}, ... , x_{930}], minibatch2: [x_{102}, x_{7}, ... , x_{1241}] \\ Epoch 2: minibatch1: [x_{3174}, x_{15}, ... , x_{412}], minibatch2: [x_{753}, x_{2447}, ... , x_{1630}]\\ Epoch 40: minibatch1: [x_{456}, x_{73}, ... , x_{1984}], minibatch2: [x_{29}, x_{675}, ... , x_{2354}]$$

TenserFlow, PyTorch, Chainer and all the good ML packages can shuffle the batches. There is a command say shuffle=True, and it is set by default.