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')
```
1bf90eff-77c5-4014-a342-abc2f6667897
قم بإنشاء وثائق تشرح الكود التالي.

```
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
46a83b64-764a-43a0-9649-7cec1a217f99
قم بإنشاء وثائق تشرح الكود التالي.

```
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
قم بإنشاء وثائق تشرح الكود التالي.

```
قطع تصحيح الطبقة:
 ديف \_\_init\_\_(ذاتي،
 الحد الأدنى\_النسبة: تعويم،
 الحد الأقصى\_النسبة: تعويم،
 حجم التصحيح\_: int = 32):
 self.min\_ratio = min\_ratio
 self.max\_ratio = max\_ratio
 self.patch\_size = patch\_size

 def \_\_call\_\_(self, x: PILImage) -> PILImage:
 إذا كان المثيل (x، PILImage):
 س = np.array(x).astype(تعويم)
 آخر:
 x = np.array(x).astype(float).transpose(1, 2, 0)
 ح، ث = س.الشكل[:2]

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