Generate documentation explaning the following code.

```
class PatchCutout:
 def \_\_init\_\_(self,
 min\_ratio: float,
 max\_ratio: float,
 patch\_size: int = 32):
 self.min\_ratio = min\_ratio
 self.max\_ratio = max\_ratio
 self.patch\_size = patch\_size

 def \_\_call\_\_(self, x: PILImage) -> PILImage:
 if isinstance(x, PILImage):
 x = np.array(x).astype(float)
 else:
 x = np.array(x).astype(float).transpose(1, 2, 0)
 h, w = x.shape[:2]

 mean\_color = x.mean(axis=(0, 1), keepdims=True)
 mask = torch.rand(1, h // self.patch\_size + 1, w // self.patch\_size + 1)
 mask = mask < random.uniform(self.min\_ratio, self.max\_ratio)
 mask = F.resize(mask.float(),
 size=(h, w),
 interpolation=InterpolationMode.NEAREST)
 mask = mask.permute(1, 2, 0).numpy()
 print(x.shape, mask.shape, mean\_color.shape)
 x = x \* (1 - mask) + mean\_color \* mask
 print(x.shape)
 return PIL.Image.fromarray(x.astype('uint8')).convert('RGB')
```
818c9e76-9b4f-40c6-8efb-2dcd6d5d5ddb
Vygenerujte dokumentaci vysvětlující následující kód.

```
class PatchCutout:
 def \_\_init\_\_(self,
 min\_ratio: float,
 max\_ratio: float,
 patch\_size: int = 32):
 self.min\_ratio = min\_ratio
 self.max\_ratio = max\_ratio
 self.patch\_size = patch\_size

 def \_\_call\_\_(self, x: PILImage) -> PILImage:
 if isinstance(x, PILImage):
 x = np.array(x).astype(float)
 else:
 x = np.array(x).astype(float).transpose(1, 2, 0)
 h, w = x.shape[:2]

 mean\_color = x.mean(axis=(0, 1), keepdims=True)
 mask = torch.rand(1, h // self.patch\_size + 1, w // self.patch\_size + 1)
 mask = mask < random.uniform(self.min\_ratio, self.max\_ratio)
 mask = F.resize(mask.float(),
 size=(h, w),
 interpolation=InterpolationMode.NEAREST)
 mask = mask.permute(1, 2, 0).numpy()
 print(x.shape, mask.shape, mean\_color.shape)
 x = x \* (1 - mask) + mean\_color \* mask
 print(x.shape)
 return PIL.Image.fromarray(x.astype('uint8')).convert('RGB')
```
submitted
Vygenerujte dokumentaci vysvětlující následující kód.

```
class PatchCutout:
 def \_\_init\_\_(self,
 min\_ratio: float,
 max\_ratio: float,
 patch\_size: int = 32):
 self.min\_ratio = min\_ratio
 self.max\_ratio = max\_ratio
 self.patch\_size = patch\_size

 def \_\_call\_\_(self, x: PILImage) -> PILImage:
 if isinstance(x, PILImage):
 x = np.array(x).astype(float)
 else:
 x = np.array(x).astype(float).transpose(1, 2, 0)
 h, w = x.shape[:2]

 mean\_color = x.mean(axis=(0, 1), keepdims=True)
 mask = torch.rand(1, h // self.patch\_size + 1, w // self.patch\_size + 1)
 mask = mask < random.uniform(self.min\_ratio, self.max\_ratio)
 mask = F.resize(mask.float(),
 size=(h, w),
 interpolation=InterpolationMode.NEAREST)
 mask = mask.permute(1, 2, 0).numpy()
 print(x.shape, mask.shape, mean\_color.shape)
 x = x \* (1 - mask) + mean\_color \* mask
 print(x.shape)
 return PIL.Image.fromarray(x.astype('uint8')).convert('RGB')
```
{"source": "ewof/sharegpt-instruct-unfiltered-deduped", "kind": "human", "evolved_from": null}