Use new_empty in dropout (pytorch#72078)

Summary:
This will be needed by functorch to have the expected behavior of randomness:
Dropout generates a tensor of the right size and then calls `bernoulli_` on that. In order to get the expected behavior from ensembled creation, we'll need to make sure that the generated tensor is a batched tensor.This works mostly because most tensors are created as `empty_like` but this one just creates `empty` because it needs a new shape, only for feature dropout. There is also no analogous version in CUDA because this directly calls`_dropout_impl` here (not in native_functions.yaml)

This shouldn't change the behavior outside of functorch

Pull Request resolved: pytorch#72078

Reviewed By: zou3519

Differential Revision: D33898338

Pulled By: samdow

fbshipit-source-id: 9d9ed59d138d732d9647b2771ccf2ea97cffae1c
(cherry picked from commit e51cf3e)

Author: Samantha Andow

aten/src/ATen/native/Dropout.cpp

@@ -22,7 +22,7 @@ Tensor make_feature_noise(const Tensor& input) {
     (void)i; //Suppress unused variable warning
     sizes.push_back(1);
   }
-  return at::empty(sizes, input.options());
+  return input.new_empty(sizes);
 }
 
 bool is_fused_kernel_acceptable(const Tensor& input, double p) {