[mlir][linalg] Add scalar broadcast load case to the vectoriser

This patch extends the Linalg vectoriser so that scalar loads are
correctly identified as scalar rather than gather loads. Below is an
example of a scalar load (note that both indices are loop invariant):
```
func.func @example(%arg0: tensor<80x16xf32>, %arg2: tensor<1x4xf32>) -> tensor<1x4xf32> {
%c8 = arith.constant 8 : index
%c16 = arith.constant 16 : index
%1 = linalg.generic {
    indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
    iterator_types = ["parallel", "parallel"]
  } outs(%arg2 : tensor<1x4xf32>) {
  ^bb0(%out: f32):
    %2 = linalg.index 0 : index
    %extracted = tensor.extract %arg0[%2, %c16] : tensor<80x16xf32>
    linalg.yield %extracted : f32
  } -> tensor<1x4xf32>
  return %1 : tensor<1x4xf32>
}
```

This patch also makes sure that these scalar loads are indeed lowered to
a scalar load followed by a broadcast:
```
    %extracted = tensor.extract %arg0[%1, %c16] : tensor<80x16xf32>
    %2 = vector.broadcast %extracted : f32 to vector<1x4xf32>
```

Differential Revision: https://reviews.llvm.org/D149678