docs: improve documentation

DESCRIPTION

@@ -33,7 +33,7 @@ Description: Deep Learning library that extends the mlr3 framework by building
   defined in 'mlr3pipelines'.
 License: LGPL (>= 3)
 Depends:
-    mlr3 (>= 0.16.0),
+    mlr3 (>= 0.16.1),
     mlr3pipelines (>= 0.5.0),
     torch (>= 0.11.0),
     R (>= 3.5.0)

R/DataBackendLazy.R

@@ -21,6 +21,13 @@
 #'
 #' Beware that accessing the backend's hash also contructs the backend.
 #'
+#' **Important**
+#'
+#' When the constructor generates `factor()` variables it is important that the ordering of the levels in data
+#' corresponds to the ordering of the levels in the `col_info` argument.
+#' Because the ordering of the level depends on the locale, it is recommended to e.g. use the `C` locale in the
+#' `constructor` function, e.g. with `withr::with_locale()`.
+#'
 #' @param constructor (`function()`)\cr
 #'   A function with no arguments, whose return value must be the actual backend.
 #'   This function is called the first time the field `$backend` is accessed.

R/PipeOpModule.R

@@ -8,7 +8,7 @@
 #' represents a neural network architecture. Such a graph can also be used to create a [`nn_graph`] which inherits
 #' from [`nn_module`].
 #'
-#' In most cases it is easier to create such a network by creating a isomorphic graph consisting
+#' In most cases it is easier to create such a network by creating a structurally related graph consisting
 #' of nodes of class [`PipeOpTorchIngress`] and [`PipeOpTorch`]. This graph will then generate the graph consisting
 #' of `PipeOpModule`s as part of the [`ModelDescriptor`].
 #'

R/PipeOpTorch.R

@@ -5,7 +5,7 @@
 #' @description
 #' `PipeOpTorch` is the base class for all [`PipeOp`]s that represent neural network layers in a [`Graph`].
 #' During **training**, it generates a [`PipeOpModule`] that wraps an [`nn_module`][torch::nn_module] and attaches it
-#' to the isomorphic architecture, which is also represented as a [`Graph`] consisting mostly of [`PipeOpModule`]s
+#' to the architecture, which is also represented as a [`Graph`] consisting mostly of [`PipeOpModule`]s
 #' an [`PipeOpNOP`]s.
 #'
 #' While the former [`Graph`] operates on [`ModelDescriptor`]s, the latter operates on [tensors][torch_tensor].
@@ -74,7 +74,7 @@
 #' `id` and the `channel` name of the sending `PipeOp` in the slot `.pointer`.
 #'
 #' The new graph in the [`model_descriptor_union`] represents the current state of the neural network
-#' architecture. It is isomorphic to the subgraph that consists of all pipeops of class `PipeOpTorch` and
+#' architecture. It is structurally similar to the subgraph that consists of all pipeops of class `PipeOpTorch` and
 #' [`PipeOpTorchIngress`] that are ancestors of this `PipeOpTorch`.
 #'
 #' For the output, a shallow copy of the [`ModelDescriptor`] is created and the `.pointer` and