Quickstart: Upgrade section "Training" to new code (#1663)

Co-authored-by: Dave Farago <dfarago@innoopract.com>

docs/quickstart.md

@@ -141,18 +141,15 @@ For our loss function we will be using sparse categorical cross entropy loss.
 
 ```python
 # from tinygrad.tensor import sparse_categorical_crossentropy
-def sparse_categorical_crossentropy(out, Y, ignore_index=-1):
+def sparse_categorical_crossentropy(self, Y, ignore_index=-1) -> Tensor:
     loss_mask = Y != ignore_index
-  num_classes = out.shape[-1]
-  y_counter = Tensor.arange(num_classes, requires_grad=False).unsqueeze(0).expand(Y.numel(), num_classes)
-  y = (y_counter == Y.flatten().reshape(-1, 1)).where(-1.0, 0)
-  y = y * loss_mask.reshape(-1, 1)
-  y = y.reshape(*Y.shape, num_classes)
-  return out.log_softmax().mul(y).sum() / loss_mask.sum()
+    y_counter = Tensor.arange(self.shape[-1], dtype=dtypes.int32, requires_grad=False, device=self.device).unsqueeze(0).expand(Y.numel(), self.shape[-1])
+    y = ((y_counter == Y.flatten().reshape(-1, 1)).where(-1.0, 0) * loss_mask.reshape(-1, 1)).reshape(*Y.shape, self.shape[-1])
+    return self.log_softmax().mul(y).sum() / loss_mask.sum()
 ```
 
 As we can see in this implementation of cross entropy loss, there are certain operations that tinygrad does not support.
-Namely, operations that are load/store like indexing a tensor with another tensor or assigning a value to a tensor at a certain index.
+Namely, operations that are load/store or assigning a value to a tensor at a certain index.
 Load/store ops are not supported in tinygrad because they add complexity when trying to port to different backends and 90% of the models out there don't use/need them.
 
 For our optimizer we will be using the traditional stochastic gradient descent optimizer with a learning rate of 3e-4.