updated readme for vit

ivy_models/vit/README.rst

@@ -50,6 +50,30 @@ Getting started
     # Instantiate vit_h_14 model
     ivy_vit_h_14 = vit_h_14(pretrained=True)
 
+    # Convert the Torch image tensor to an Ivy tensor and adjust dimensions
+    img = ivy.asarray(torch_img.permute((0, 2, 3, 1)), dtype="float32", device="gpu:0")
+
+    # Compile the Ivy vit_h_14 model with the Ivy image tensor
+    ivy_vit_h_14.compile(args=(img,))
+
+    # Pass the Ivy image tensor through the Ivy vit_h_14 model and apply softmax
+    output = ivy.softmax(ivy_vit_h_14(img))
+
+    # Get the indices of the top 3 classes from the output probabilities
+    classes = ivy.argsort(output[0], descending=True)[:3] 
+
+    # Retrieve the logits corresponding to the top 3 classes
+    logits = ivy.gather(output[0], classes) 
+
+    print("Indices of the top 3 classes are:", classes)
+    print("Logits of the top 3 classes are:", logits)
+    print("Categories of the top 3 classes are:", [categories[i] for i in classes.to_list()])
+
+
+    `Indices of the top 3 classes are: ivy.array([457, 655, 691], dev=gpu:0)``
+    `Logits of the top 3 classes are: ivy.array([0.03149041, 0.02733098, 0.02412809], dev=gpu:0)``
+    `Categories of the top 3 classes are: ['bow tie', 'miniskirt', 'oxygen mask']``
+
 The pretrained vit_h_14 model is now ready to be used, and is compatible with any other PyTorch code
 
 Citation