diff --git a/torchgeo/transforms/temporal.py b/torchgeo/transforms/temporal.py
index f5ecaf39f30..813bb187eec 100644
--- a/torchgeo/transforms/temporal.py
+++ b/torchgeo/transforms/temporal.py
@@ -4,8 +4,9 @@
 """TorchGeo temporal transforms."""
 
 from typing import Any, Literal
-from einops import rearrange
+
 import kornia.augmentation as K
+from einops import rearrange
 from torch import Tensor
 
 
@@ -19,7 +20,7 @@ class TemporalRearrange(K.IntensityAugmentationBase2D):
 
     def __init__(
         self,
-        mode: Literal["merge", "split"],
+        mode: Literal['merge', 'split'],
         num_temporal_channels: int,
         p: float = 1.0,
         p_batch: float = 1.0,
@@ -40,19 +41,12 @@ def __init__(
         super().__init__(
             p=p, p_batch=p_batch, same_on_batch=same_on_batch, keepdim=keepdim
         )
-        if mode not in ["merge", "split"]:
+        if mode not in ['merge', 'split']:
             raise ValueError("mode must be either 'merge' or 'split'")
 
-        self.flags = {
-            "mode": mode,
-            "num_temporal_channels": num_temporal_channels,
-        }
+        self.flags = {'mode': mode, 'num_temporal_channels': num_temporal_channels}
 
-    def apply_transform(
-        self,
-        input: Tensor,
-        flags: dict[str, Any],
-    ) -> Tensor:
+    def apply_transform(self, input: Tensor, flags: dict[str, Any]) -> Tensor:
         """Apply the transform.
 
         Args:
@@ -65,25 +59,25 @@ def apply_transform(
         Raises:
             ValueError: If input tensor dimensions don't match expected shape
         """
-        mode = flags["mode"]
-        t = flags["num_temporal_channels"]
+        mode = flags['mode']
+        t = flags['num_temporal_channels']
 
-        if mode == "merge":
+        if mode == 'merge':
             if input.ndim != 5:
                 raise ValueError(
-                    f"Expected 5D input tensor (B,T,C,H,W), got shape {input.shape}"
+                    f'Expected 5D input tensor (B,T,C,H,W), got shape {input.shape}'
                 )
-            return rearrange(input, "b t c h w -> b (t c) h w")
+            return rearrange(input, 'b t c h w -> b (t c) h w')
         else:
             if input.ndim != 4:
                 raise ValueError(
-                    f"Expected 4D input tensor (B,TC,H,W), got shape {input.shape}"
+                    f'Expected 4D input tensor (B,TC,H,W), got shape {input.shape}'
                 )
             tc = input.shape[1]
             if tc % t != 0:
                 raise ValueError(
-                    f"Input channels ({tc}) must be divisible by "
-                    f"num_temporal_channels ({t})"
+                    f'Input channels ({tc}) must be divisible by '
+                    f'num_temporal_channels ({t})'
                 )
             c = tc // t
-            return rearrange(input, "b (t c) h w -> b t c h w", t=t, c=c)
\ No newline at end of file
+            return rearrange(input, 'b (t c) h w -> b t c h w', t=t, c=c)