--- a
+++ b/reproducibility/embedders/transform.py
@@ -0,0 +1,52 @@
+from torchvision.transforms import (
+    RandomAffine,
+    RandomPerspective,
+    RandomAutocontrast,
+    RandomEqualize,
+    RandomRotation,
+    RandomCrop,
+    RandomHorizontalFlip
+)
+from torchvision.transforms import InterpolationMode
+BICUBIC = InterpolationMode.BICUBIC
+from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
+
+def _convert_image_to_rgb(image):
+    return image.convert("RGB")
+
+
+def _train_transform(first_resize=512,
+                    n_px=224,
+                    ):
+    return Compose([
+        Resize([first_resize], interpolation=InterpolationMode.BICUBIC),# Desired output size. If size is a sequence like (h, w), output size will be matched to this. If size is an int, smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).
+        RandomCrop([n_px]),
+        RandomHorizontalFlip(),
+        RandomAffine(
+            degrees=10,
+            translate=(0.1, 0.1),
+            scale=(0.8, 1.2),
+            shear=(-15, 15, -15, 15),
+            interpolation=InterpolationMode.BILINEAR,
+            fill=127,
+        ),
+        RandomPerspective(
+            distortion_scale=0.3,
+            p=0.3,
+            interpolation=InterpolationMode.BILINEAR,
+            fill=127,
+        ),
+        _convert_image_to_rgb,
+        ToTensor(),
+        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+    ])
+
+
+def _transform(n_px):
+    return Compose([
+        Resize(n_px, interpolation=BICUBIC),
+        CenterCrop(n_px),
+        _convert_image_to_rgb,
+        ToTensor(),
+        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+    ])