code: def __call__(self, *args, **kwargs) -> ReturnType:
      # all inputs (except const) are realized
      input_tensors: Dict[
          Union[int, str], Union[LazyBuffer, MultiLazyBuffer]
      ] = {
          cast(Union[int, str], k): v.realize().lazydata
          for k, v in itertools.chain(enumerate(args), kwargs.items())
          if v.__class__ is Tensor
      }  # noqa: E501
      expected_name_sts_dtype_device = tuple(
          [
              (
                  k,
                  v.st.unbind()[0]
                  if isinstance(v, LazyBuffer)
                  else ShapeTracker.from_shape(v.shape),
                  v.dtype,
                  v.device,
              )
              for k, v in input_tensors.items()
          ]
      )  # noqa: E501
  
      # get rawbuffers
      lbs: List[LazyBuffer] = [
          v for v in input_tensors.values() if isinstance(v, LazyBuffer)
      ] + flatten(
          [
              mlb.lbs
              for mlb in input_tensors.values()
              if isinstance(mlb, MultiLazyBuffer)
          ]
      )  # noqa: E501
      input_rawbuffers: List[Buffer] = [
          v.base.realized for v in lbs if v.base.realized is not None
      ]
      assert len(set(input_rawbuffers)) == len(
          input_rawbuffers
      ), "duplicate inputs to JIT"
  
      # get variables: they can either be in Tensors or passed in as arguments, and all must be bound. these are all global
      var_vals: Dict[Variable, int] = merge_dicts(
          [arg.st.var_vals for arg in lbs]
          + [
              dict(
                  x.unbind()
                  for x in itertools.chain(args, kwargs.values())
                  if isinstance(x, Variable)
              )
          ]
      )  # noqa: E501
      expected_vals = tuple(var_vals.keys())
  
      if self.cnt >= 2:
          # jit exec
          assert (
              self.expected_vals == expected_vals
              and self.expected_name_sts_dtype_device is not None
          ), "missing/mismatch of var_vals"
          assert all(
              x[0] == y[0]
              and x[1].views == y[1].views
              and x[2] == y[2]
              and x[3] == y[3]
              for x, y in zip(
                  self.expected_name_sts_dtype_device,
                  expected_name_sts_dtype_device,
              )
          ), f"mismatch of input tensors, expected {self.expected_name_sts_dtype_device} got {expected_name_sts_dtype_device}"
          for (j, i), input_idx in self.input_replace.items():
              self.jit_cache[j].rawbufs[i] = input_rawbuffers[input_idx]
          for ji in self.jit_cache:
              ji.prg(
                  cast(List[Buffer], ji.rawbufs),
                  var_vals,
                  wait=DEBUG >= 2,
                  jit=True,
              )
      elif self.cnt == 1:
          # jit capture
          self.expected_vals, self.expected_name_sts_dtype_device = (
              expected_vals,
              expected_name_sts_dtype_device,
          )
          CacheCollector.start(var_vals)
          with Context(GRAPH=getenv("JITGRAPH", GRAPH.value)):
              self.ret = self.fxn(*args, **kwargs)
              for p in get_parameters(self.ret):
                  p.realize()
          self.jit_cache = CacheCollector.finish()
          assert len(self.jit_cache) != 0, "didn't JIT anything!"
          if DEBUG >= 1 and len(
              set(
                  get_input_replace(
                      self.jit_cache, input_rawbuffers
                  ).values()
              )
          ) != len(input_rawbuffers):
              print("WARNING: some input tensors not found")
          if DEBUG >= 1:
              print(
                  f"JIT captured {len(self.jit_cache)} kernels with {len(input_rawbuffers)} inputs"
              )
  
          # Condense the items into a graph executor.
          if getenv("JIT") != 2:
              # Split JIT cache into batches for faster graph execution.
              # This allows the accelerator to run some batches while subsequent graphs are still being updated.
              graphed_jit_cache: List[JitItem] = []
              current_batch: List[JitItem] = []
              current_device: Union[Compiled, None] = None
  
              # Flush the current batch.
              def flush():
                  nonlocal current_batch, current_device
                  assert current_device is not None
                  try:
                      graphed_jit_cache.append(
                          JitItem(
                              current_device.graph(
                                  current_batch,
                                  input_rawbuffers,
                                  var_vals,
                              ),
                              cast(
                                  List[Optional[Buffer]],
                                  input_rawbuffers,
                              ),
                          )
                      )  # noqa: E501
                      if DEBUG >= 2:
                          print(
                              f"\tJIT GRAPHing batch with {len(current_batch)} kernels on device {current_device}"
                          )
                  except GraphException as e:
                      graphed_jit_cache.extend(current_batch)
                      if DEBUG >= 2:
                          print(
                              f"\tJIT GRAPHing failed batch with {len(current_batch)} kernels on device {current_device}: {e}"
                          )
                  current_batch = []
                  current_device = None
  
              for i, ji in enumerate(self.jit_cache):
                  # If the jit item can potentially be graphed, put it in a batch.
                  can_be_graphed = (
                      isinstance(ji.prg, CompiledASTRunner)
                      and ji.prg.device.graph
                  )
                  if can_be_graphed:
                      assert isinstance(ji.prg, CompiledASTRunner)
                      # If the device changed we flush the batch early and append this item for the next batch.
                      if (
                          current_device is not None
                          and ji.prg.device != current_device
                      ):
                          flush()
                      current_device = ji.prg.device
                      current_batch.append(ji)
  
                  # The flush is done when (1) ji is the last one, (2) the size of batch exceeds the maximum batch size or
                  # (3) the current jit item cannot be graphed, so the current batch is flushed before such a jit item is added.
                  if len(current_batch) > 0 and (
                      i == len(self.jit_cache) - 1
                      or len(current_batch)
                      >= getenv("JIT_BATCH_SIZE", 64)
                      or not can_be_graphed
                  ):
                      flush()  # noqa: E501
  
                  # If the jit item cannot be graphed, put it right into the final cache after the flush.
                  if not can_be_graphed:
                      graphed_jit_cache.append(ji)
  
              self.jit_cache = graphed_jit_cache
  
          self.input_replace = get_input_replace(
              self.jit_cache, input_rawbuffers
          )
      elif self.cnt == 0:
          # jit ignore
          self.ret = self.fxn(*args, **kwargs)
  
      # clear jit inputs
      for j, i in self.input_replace.keys():
          self.jit_cache[j].rawbufs[i] = None
  
      self.cnt += 1
      return cast(ReturnType, self.ret)
  
imports: from __future__ import annotations
  from typing import Callable, List, Tuple, Dict, cast, Union, Optional, TypeVar, Generic
  import functools, itertools, operator
  from tinygrad.nn.state import get_parameters
  from tinygrad.dtype import DType
  from tinygrad.helpers import DEBUG, merge_dicts, getenv, all_int, Context, GRAPH, flatten
  from tinygrad.device import Compiled, JITRunner, CompiledASTRunner, Buffer
  from tinygrad.tensor import Tensor
  from tinygrad.lazy import LazyBuffer
  from tinygrad.features.multi import MultiLazyBuffer
  from tinygrad.shape.shapetracker import ShapeTracker
  from tinygrad.shape.symbolic import Variable, NumNode, Node
  from weakref import ref, WeakKeyDictionary
  from dataclasses import dataclass
label_desc: Use image node_id 4 for calling the TinyJit obj's underlying member method code with example usage: obj.__call__() and returns: cast