train_data = single(permute(reshape(train.data',32,32,3,[]),[2 1 3 4]));

This line of code is reshaping the input data for a neural network.

The `train.data` variable is assumed to be a 4D array with dimensions `32 x 32 x 3 x N`, where `N` is the number of training examples.

The `reshape` function is used to reorder the dimensions of this array. It first transposes the array so that the third dimension becomes the first dimension, i.e. `train.data'` has dimensions `N x 3 x 32 x 32`. The `reshape` function then rearranges the dimensions so that the second dimension becomes the first dimension, the first dimension becomes the second dimension, and the third and fourth dimensions are combined into a single dimension.

The resulting array has dimensions `32 x 32 x 3 x N`, which is a more suitable format for feeding into a convolutional neural network (CNN), as it allows the network to treat each pixel in the image as a separate input channel.

Finally, the `single` function is used to convert the array to single-precision floating point values, which is a common data type used in CNNs.

Overall, this line of code is preparing the input data for a CNN by reshaping and converting the data to the appropriate format and data type.