Backport PR #3331 on branch 1.3.x (update bug of installation with custom dataloder) (#3332)

Backport PR #3331: update bug of installation with custom dataloder

Co-authored-by: Ori Kronfeld <[email protected]>

Author: meeseeksmachine

docs/api/developer.md

@@ -185,6 +185,7 @@ Module classes in the external API with respective generative and inference proc
    external.methylvi.METHYLANVAE
    external.decipher.DecipherPyroModule
    external.resolvi.RESOLVAE
+   external.totalanvi.TOTALANVAE
    external.sysvi.SysVAE
 
 ```

docs/api/user.md

@@ -62,6 +62,7 @@ import scvi
    external.METHYLVI
    external.METHYLANVI
    external.Decipher
+   external.TOTALANVI
    external.RESOLVI
    external.SysVI
 ```

src/scvi/dataloaders/_custom_dataloders.py

@@ -16,13 +16,13 @@
 if TYPE_CHECKING:
     from typing import Any
 
+    import lamindb as ln
     import pandas as pd
+    import tiledbsoma as soma
 
 
-@dependencies("lamindb")
 class MappedCollectionDataModule(LightningDataModule):
-    import lamindb as ln
-
+    @dependencies("lamindb")
     def __init__(
         self,
         collection: ln.Collection,
@@ -353,18 +353,15 @@ def __len__(self):
             return len(self.dataloader)
 
 
-@dependencies("tiledbsoma")
-@dependencies("tiledbsoma_ml")
 class TileDBDataModule(LightningDataModule):
-    import tiledbsoma as soma
-
     """PyTorch Lightning DataModule for training scVI models from SOMA data
 
     Wraps a `tiledbsoma_ml.ExperimentDataset` to stream the results of a SOMA
     `ExperimentAxisQuery`, exposing a `DataLoader` to generate tensors ready for scVI model
     training. Also handles deriving the scVI batch label as a tuple of obs columns.
     """
 
+    @dependencies("tiledbsoma")
     def __init__(
         self,
         query: soma.ExperimentAxisQuery,
@@ -503,6 +500,7 @@ def __init__(
             accelerator=accelerator, devices=device, return_device="torch"
         )
 
+    @dependencies("tiledbsoma_ml")
     def setup(self, stage: str | None = None) -> None:
         # Instantiate the ExperimentDataset with the provided args and kwargs.
         from tiledbsoma_ml import ExperimentDataset
@@ -539,6 +537,7 @@ def setup(self, stage: str | None = None) -> None:
         else:
             self.val_dataset = None
 
+    @dependencies("tiledbsoma_ml")
     def train_dataloader(self) -> DataLoader:
         from tiledbsoma_ml import experiment_dataloader
 
@@ -547,6 +546,7 @@ def train_dataloader(self) -> DataLoader:
             **self.dataloader_kwargs,
         )
 
+    @dependencies("tiledbsoma_ml")
     def val_dataloader(self) -> DataLoader:
         from tiledbsoma_ml import experiment_dataloader