remove the base classes since we don't need to be able to extend

src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py

@@ -26,7 +26,6 @@ References:
 """
 
 import math
-from abc import ABC, abstractmethod
 from collections import deque
 
 import einops
@@ -607,28 +606,14 @@ class DiffusionTransformer(nn.Module):
 # -- Objectives --
 
 
-class BaseObjective(ABC):
-    """Base class for objectives used in Multi-Task DiT policy."""
-
-    def __init__(self, config, action_dim: int, horizon: int):
-        self.config = config
-        self.action_dim = action_dim
-        self.horizon = horizon
-
-    @abstractmethod
-    def compute_loss(self, model: nn.Module, batch: dict[str, Tensor], conditioning_vec: Tensor) -> Tensor:
-        pass
-
-    @abstractmethod
-    def conditional_sample(self, model: nn.Module, batch_size: int, conditioning_vec: Tensor) -> Tensor:
-        pass
-
-
-class DiffusionObjective(BaseObjective):
+class DiffusionObjective(nn.Module):
     """Standard diffusion (DDPM/DDIM) objective implementation."""
 
     def __init__(self, config, action_dim: int, horizon: int, do_mask_loss_for_padding: bool = False):
-        super().__init__(config, action_dim, horizon)
+        super().__init__()
+        self.config = config
+        self.action_dim = action_dim
+        self.horizon = horizon
         self.do_mask_loss_for_padding = do_mask_loss_for_padding
 
         scheduler_kwargs = {
@@ -704,11 +689,14 @@ class DiffusionObjective(BaseObjective):
         return sample
 
 
-class FlowMatchingObjective(BaseObjective):
+class FlowMatchingObjective(nn.Module):
     """Flow matching objective: trains a model to predict velocity fields."""
 
     def __init__(self, config, action_dim: int, horizon: int, do_mask_loss_for_padding: bool = False):
-        super().__init__(config, action_dim, horizon)
+        super().__init__()
+        self.config = config
+        self.action_dim = action_dim
+        self.horizon = horizon
         self.do_mask_loss_for_padding = do_mask_loss_for_padding
 
     def _sample_timesteps(self, batch_size: int, device: torch.device) -> Tensor: