In the Flux docs for `train!`

it says

In case datapoints

`d`

are of numeric array type, assume no splatting is needed and compute the gradient of`loss(d)`

.

What does “splatting” mean here?

I am *guessing* that what this means is that, if the data are expressed as matrices (dimensions X datapoints) or maybe the other way around, the loss function can be written to operate on multiple datapoints at once. Is this correct?

If so, then does Flux *assume* that if the data are in a matrix, that the loss must operate on the whole matrix at once?