code: def test_masked_upcast(self):
      layer_1 = Tensor.cat(*[Tensor.rand(5) for _ in range(4)])
      layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.rand(6, 20))
  
      s = layer_2.lazydata.schedule()[-1]
      k = Linearizer(s.ast)
      k.hand_coded_optimizations()
      assert (
          len(k.bufs) == 6
      )  # make sure all ops are done in one kernel
      # masked upcast should upcast masked axis of size 7
      # masked upcast should not upcast large (20) last axis
      # float4/other hcopt shouldn't upcast last axis, since we already have 7 upcast, and the last axis is not very contiguous
      assert k.upcasted == 1 and k.full_shape[-1] == 7
  
imports: import numpy
  import unittest, os
  from tinygrad.codegen.kernel import Opt, OptOps, tensor_cores
  from tinygrad.codegen.linearizer import Linearizer, UOp, UOps, expand_node
  from tinygrad.device import Compiled, Device, Buffer
  from tinygrad.ops import BufferOps, MemBuffer, ConstBuffer, LazyOp, LoadOps, TernaryOps
  from tinygrad.shape.shapetracker import ShapeTracker
  from tinygrad.shape.view import View
  from tinygrad.shape.symbolic import MulNode, SumNode, Variable, NumNode, Node, create_rednode
  from tinygrad.tensor import Tensor
  from tinygrad.jit import CacheCollector
  from tinygrad.realize import run_schedule
  from tinygrad.helpers import prod
  from tinygrad.dtype import dtypes
label_desc: Use image node_id 1 for calling the TestHandCodedOpts obj's underlying member method code with example usage: obj.test_masked_upcast() without return types