Quantiles PR nit fix

src/finetuning/finetuning_torch.py

@@ -13,7 +13,7 @@ import torch.distributed as dist
 import torch.nn as nn
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.data import DataLoader, Dataset
-from timesfm.pytorch_patched_decoder import _create_quantiles
+from timesfm.pytorch_patched_decoder import create_quantiles
 
 import wandb
 
@@ -267,7 +267,7 @@ class TimesFMFinetuner:
 
     loss = self.loss_fn(last_patch_pred, x_future.squeeze(-1))
     if self.config.use_quantile_loss:
-      quantiles = self.config.quantiles or _create_quantiles()
+      quantiles = self.config.quantiles or create_quantiles()
       for i, quantile in enumerate(quantiles):
         last_patch_quantile = predictions[:, -1, :, i + 1]
         loss += torch.mean(

src/timesfm/pytorch_patched_decoder.py

@@ -21,7 +21,7 @@ from torch import nn
 import torch.nn.functional as F
 
 
-def _create_quantiles() -> list[float]:
+def create_quantiles() -> list[float]:
   return [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
 
 
@@ -48,7 +48,7 @@ class TimesFMConfig:
   # Horizon length
   horizon_len: int = 128
   # quantiles
-  quantiles: List[float] = dataclasses.field(default_factory=_create_quantiles)
+  quantiles: List[float] = dataclasses.field(default_factory=create_quantiles)
   # Padding value
   pad_val: float = 1123581321.0
   # Tolerance