code: def ast_parse(
      self,
      x: LazyOp,
      acc: List[UOp],
      offs: Optional[List[int]],
      loaded_buffers: Dict[
          Union[MemBuffer, ConstBuffer, LocalBuffer], List[UOp]
      ],
      do_reduce=False,
      loop_ctx=tuple(),
      cache=None,
  ) -> List[UOp]:  # noqa: E501
      if cache is None:
          cache = {}
      if x in cache:
          return cache[x]
      if x.op in BufferOps:
          return loaded_buffers[x.arg]
      if x.op == UnaryOps.CAST:
          return [
              self.uop(
                  UOps.CAST, self.get_base_dtype(x.arg[0]), (u,), x.arg
              )
              for u in self.ast_parse(
                  x.src[0], acc, offs, loaded_buffers
              )
          ]
      if x.op in ReduceOps and not do_reduce:
          assert offs is None, "not available if we aren't doing reduce"
          return acc
      # MULACC fusion. TODO: this is copied from Interpreted
      if x.op == ReduceOps.SUM:
          if x.src[0].op == BinaryOps.MUL:
              x = LazyOp(TernaryOps.MULACC, x.src[0].src, x.arg)
          if (castop := x.src[0]).op == UnaryOps.CAST and (
              mulop := castop.src[0]
          ).op == BinaryOps.MUL:
              # MULACC with acc cast rewrite: MUL -> CAST -> SUM => CAST -> MULACC
              x = LazyOp(
                  TernaryOps.MULACC,
                  tuple(
                      LazyOp(UnaryOps.CAST, (s,), castop.arg)
                      for s in mulop.src
                  ),
                  x.arg,
              )
  
      values = [
          self.ast_parse(
              v,
              acc,
              offs,
              loaded_buffers,
              loop_ctx=loop_ctx,
              cache=cache,
          )
          for v in x.src
      ]
      ops = {
          ReduceOps.SUM: BinaryOps.ADD,
          ReduceOps.MAX: BinaryOps.MAX,
          TernaryOps.MULACC: TernaryOps.MULACC,
      }
      if x.op in ops:
          ret: List[UOp] = []
          input_acc = acc[:]
          for val, off in zip(zip(*values), cast(List[int], offs)):
              acc[off] = self.uop(
                  UOps.ALU,
                  acc[off].dtype,
                  vin=val + (acc[off],),
                  arg=ops[x.op],
              )
              ret.append(acc[off])
          for off in range(len(acc)):
              if input_acc[off] != acc[off]:
                  acc[off] = self.uop(
                      UOps.PHI,
                      input_acc[off].dtype,
                      (input_acc[off], acc[off]) + tuple(loop_ctx),
                  )
      else:
          ret = [
              self.uop(
                  UOps.ALU,
                  dtypes.bool
                  if x.op in (BinaryOps.CMPLT, BinaryOps.CMPEQ)
                  else val[-1].dtype,
                  val,
                  x.op,
              )
              for val in zip(*values)
          ]
      cache[x] = ret
      return ret
  
imports: from __future__ import annotations
  from typing import List, Tuple, Any, Optional, cast, DefaultDict, Dict, Union, Sequence, Final, Set, Iterator
  import itertools, math, functools
  from collections import defaultdict
  from enum import Enum, auto
  from dataclasses import dataclass
  from tinygrad.dtype import ImageDType, dtypes, DType, PtrDType
  from tinygrad.helpers import colored, DEBUG, prod, getenv, all_same, to_function_name, flatten
  from tinygrad.ops import LazyOp, UnaryOps, BinaryOps, TernaryOps, ReduceOps, ConstBuffer, MemBuffer, BufferOps, get_lazyop_info
  from tinygrad.shape.shapetracker import ShapeTracker
  from tinygrad.shape.symbolic import Variable, NumNode, Node, SumNode, MulNode, DivNode, ModNode, LtNode, AndNode
  from tinygrad.codegen.kernel import LocalBuffer, Kernel
  from tinygrad.features.image import to_image_idx
label_desc: Use image node_id 10 for calling the Linearizer obj's underlying member method code with example usage: obj.ast_parse(x, acc, offs, loaded_buffers, do_reduce, loop_ctx, cache) and returns: ret, cache, loaded_buffers, unknown, acc