added gitignore, smoothened structure slightly

app/main.py

@@ -77,10 +77,10 @@ from pathlib import Path
 from typing import NamedTuple
 
 import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader, Dataset
+import torch # type: ignore
+import torch.nn as nn # type: ignore
+import torch.nn.functional as F # type: ignore
+from torch.utils.data import DataLoader, Dataset # type: ignore
 
 
 # ---------------------------------------------------------------------------
@@ -134,14 +134,9 @@ class UpBlock(nn.Module):
     avoids checkerboard artefacts common in urban grid-like rasters.
     """
 
-    def __init__(
-        self,
-        in_ch: int,
-        skip_ch: int,
-        out_ch: int,
-        dropout_p: float = 0.0,
-    ):
+    def __init__(self, in_ch: int, skip_ch: int, out_ch: int, dropout_p: float = 0.0):
         super().__init__()
+
         self.up = nn.Sequential(
             nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False),
             nn.Conv2d(in_ch, out_ch, kernel_size=1, bias=False),
@@ -231,9 +226,7 @@ class UrbanClimateUNet(nn.Module):
         # ── Bottleneck ───────────────────────────────────────────────────────
         # Receives: enc4-pooled (c*8 = 512 ch) + coarse context (128 ch)
         #           → 640 ch input, 512 ch output
-        self.bottleneck = ConvBlock(
-            c * 8 + coarse_ch, c * 8, dropout_p=dropout_bot
-        )
+        self.bottleneck = ConvBlock(c * 8 + coarse_ch, c * 8, dropout_p=dropout_bot)
 
         # ── Decoder ──────────────────────────────────────────────────────────
         # UpBlock(in_ch, skip_ch, out_ch):