code: def minifier(
      fail_f: fx.GraphModule,
      inps,
      module_fails,
      dump_state: Callable = dump_state,
      *,
      save_dir=None,
      offload_to_disk=False,
      skip_offload=False,
      skip_sanity=False,
      max_granularity=None,
  ):
      """
      Minimizes a FX graph with given inputs, such that the resulting FX graph still returns True for module_fails.
  
      Does 2 main strategies:
      1. Truncates suffix: Removes some suffix from the graph and sets a new output.
      2. Delta Debugging: Tries replacing half of the graph with inputs. If fails,
          tries replacing quarter of the graph, etc.
  
      >>> # xdoctest: +SKIP(failing)
      >>> failing_function = fx.symbolic_trace(f)
      >>> minimize(failing_function, [torch.randn(5)], lambda fx_g, inps: fx_g(*inps))
  
      note: module_fails returns True if it fails.
      """
      assert isinstance(inps, (tuple, list))
  
      failing_graph = fail_f.graph
      cur_size = len(failing_graph.nodes)
  
      if max_granularity is not None and not is_power_of_two(
          max_granularity
      ):
          raise RuntimeError(
              f"max_granularity {max_granularity} not power of two"
          )
  
      num_queries = 0
  
      def deepcopy_fx_graph(fx_graph):
          return fx.GraphModule(fail_f, copy.deepcopy(fx_graph)).graph
  
      def graph_fails(graph, inps):
          nonlocal num_queries
          graph = copy.deepcopy(graph)
          num_queries += 1
          mod = fx.GraphModule(fail_f, graph)
          mod.graph.lint()
          return module_fails(mod, inps)
  
      writer = None
      if offload_to_disk:
          writer = ContentStoreWriter(save_dir)
  
      ConcreteProp(
          fail_f, writer=writer, skip_offload=skip_offload
      ).propagate(*inps)
      if not skip_sanity and not graph_fails(failing_graph, inps):
          raise RuntimeError("Input graph did not fail the tester")
      print(f"Started off with {cur_size} nodes", file=sys.stderr)
  
      def _register_strategy(strategy: Callable, name: str):
          @wraps(strategy)
          def new_func(old_state: ReproState, granularity=1):
              print(file=sys.stderr)
              print(
                  f"Strategy: {name} (G: {granularity}) "
                  f"({len(old_state.graph.nodes)} nodes, {len(old_state.inps)} inputs)",
                  file=sys.stderr,
              )
              new_state = strategy(
                  deepcopy_fx_graph(old_state.graph),
                  list(old_state.inps),
                  granularity,
              )
              if new_state is not None:
                  new_nodes = len(new_state.graph.nodes)
                  old_nodes = len(old_state.graph.nodes)
                  new_inps = len(new_state.inps)
                  old_inps = len(old_state.inps)
                  new_outs = len(get_outputs(new_state.graph))
                  old_outs = len(get_outputs(old_state.graph))
                  progress_made = False
                  if new_nodes < old_nodes:
                      progress_made = True
                      print(
                          f"SUCCESS: Went from {old_nodes} to {new_nodes} nodes",
                          file=sys.stderr,
                      )
                  if new_inps > old_inps:
                      progress_made = True
                      print(
                          f"SUCCESS: Went from {old_inps} to {new_inps} inputs",
                          file=sys.stderr,
                      )
                  if new_outs < old_outs:
                      progress_made = True
                      print(
                          f"SUCCESS: Went from {old_outs} to {new_outs} outputs",
                          file=sys.stderr,
                      )
  
                  if not progress_made:
                      raise RuntimeError(
                          "Success raised but no progress made?"
                      )
  
                  if not graph_fails(new_state.graph, new_state.inps):
                      print(
                          "WARNING: Something went wrong, not applying this minification",
                          file=sys.stderr,
                      )
                      return None
                  return new_state
              else:
                  print(f"FAIL: {name}", file=sys.stderr)
              return None
  
          return new_func
  
      def register_strategy(name: str):
          return partial(_register_strategy, name=name)
  
      @register_strategy("Truncate suffix")
      def remove_suffix(cur_graph, cur_inps, granularity):
          tested = set()
          new_graph = fx.Graph()
          env = {}
          for idx, node in enumerate(cur_graph.nodes):
              new_node = new_graph.node_copy(node, lambda x: env[x])
              if node.op not in ["placeholder", "output"]:
                  # If idx is divisible by (granularity * 2), it would have been checked already.
                  if (
                      idx % granularity == 0
                      and (idx % (granularity * 2) != 0)
                      and idx not in tested
                  ):
                      output_node = new_graph.output((new_node,))
                      if len(new_graph.nodes) < len(
                          cur_graph.nodes
                      ) and graph_fails(new_graph, cur_inps):
                          return ReproState(new_graph, cur_inps)
                      else:
                          tested.add(idx)
                          new_graph.erase_node(output_node)
              env[node] = new_node
          return None
  
      @register_strategy("Remove outputs")
      def remove_outputs(cur_graph, cur_inps, granularity):
          granularity = max(1, granularity // 2)
          for idx, node in enumerate(cur_graph.nodes):
              node.idx = idx
              if node.op == "output":
                  output = node
                  break
  
          if isinstance(output.args[0], fx.Node):
              return None
  
          output_args = sorted(
              output.args[0],
              key=lambda x: x.idx
              if isinstance(x, fx.Node)
              else int(1e9),
          )
          if len(output_args) == 1:
              return None
  
          for idx in range(0, len(output_args), granularity):
              output.args = (
                  output_args[:idx] + output_args[idx + granularity :],
              )
              if graph_fails(cur_graph, cur_inps):
                  return ReproState(cur_graph, cur_inps)
          return None
  
      def remove_unused_inputs_unchecked(cur_state: ReproState):
          cur_graph = cur_state.graph
          cur_inps = cur_state.inps
          ph_nodes = get_placeholders(cur_graph)
          assert len(ph_nodes) == len(cur_inps)
  
          new_inps = []
          for idx in range(len(ph_nodes)):
              if len(ph_nodes[idx].users) == 0:
                  cur_graph.erase_node(ph_nodes[idx])
              else:
                  new_inps.append(cur_inps[idx])
          if len(new_inps) < len(cur_inps):
              return ReproState(cur_graph, new_inps)
          return None
  
      def remove_unused_inputs_checked(cur_state: ReproState):
          new_state = remove_unused_inputs_unchecked(cur_state)
          if new_state is not None and graph_fails(
              new_state.graph, new_state.inps
          ):
              return new_state
          return None
  
      def _remove_unused_wrapper(cur_graph, cur_inps, granularity):
          return remove_unused_inputs_checked(
              ReproState(cur_graph, cur_inps)
          )
  
      remove_unused_inputs = register_strategy("Remove unused inputs")(
          _remove_unused_wrapper
      )
  
      @register_strategy("Eliminate dead code")
      def eliminate_dead_code(cur_graph, cur_inps, granularity):
          if cur_graph.eliminate_dead_code() and graph_fails(
              cur_graph, cur_inps
          ):
              return ReproState(cur_graph, cur_inps)
          return None
  
      def _consolidate_placeholders(cur_graph, inps):
          new_graph = fx.Graph()
          env = {}
          seen_non_placeholder = False
  
          # Move all placeholders to the front; also, if any load_tensor
          # is at the front, convert it into an input (because it can be live
          # all the time)
          for node in cur_graph.nodes:
              if node.op == "placeholder":
                  new_node = new_graph.node_copy(node, lambda x: env[x])
                  env[node] = new_node
              elif not seen_non_placeholder and is_load_tensor_node(
                  node
              ):
                  new_node = new_graph.placeholder(node.name)
                  env[node] = new_node
                  inps.append(
                      torch.ops.debugprims.load_tensor.default(
                          *node.args, **node.kwargs
                      )
                  )
              else:
                  seen_non_placeholder = True
  
          # Move everyone else
          for node in cur_graph.nodes:
              if node not in env:
                  new_node = new_graph.node_copy(node, lambda x: env[x])
                  env[node] = new_node
          return new_graph
  
      @register_strategy("Delta Debugging")
      def delta_debugging(cur_graph: fx.Graph, cur_inps, granularity):
          num_nodes = len(cur_graph.nodes)
          for start_range in range(0, num_nodes, granularity):
              is_removing = False
              new_graph = deepcopy_fx_graph(cur_graph)
              new_inps = cur_inps[:]
              end_range = min(num_nodes, start_range + granularity)
              for idx in range(start_range, end_range):
                  new_node = list(new_graph.nodes)[idx]
                  if _convert_node_to_placeholder(
                      new_graph, new_node, new_inps
                  ):
                      is_removing = True
              if not is_removing:
                  continue
              new_graph.eliminate_dead_code()
              new_graph = _consolidate_placeholders(new_graph, new_inps)
              new_state = remove_unused_inputs_unchecked(
                  ReproState(new_graph, new_inps)
              )
              if new_state is None:
                  new_state = ReproState(new_graph, new_inps)
              if graph_fails(new_state.graph, new_state.inps):
                  return ReproState(new_state.graph, new_state.inps)
  
          return None
  
      @register_strategy("Consolidate Inputs")
      def consolidate_inputs(cur_graph, cur_inps, granularity):
          old_len = len(cur_inps)
          cur_graph = _consolidate_placeholders(cur_graph, cur_inps)
          if len(cur_inps) > old_len and graph_fails(
              cur_graph, cur_inps
          ):
              return ReproState(cur_graph, cur_inps)
          return None
  
      failing_state = ReproState(failing_graph, inps)
  
      def try_granularity(failing_state, granularity, use_non_granular):
          print(f"Trying granularity {granularity}", file=sys.stderr)
  
          strategies = []
          num_nodes = len(failing_state.graph.nodes)
          num_outputs = len(get_outputs(failing_state.graph))
          if num_outputs > num_nodes // 2:
              strategies += [remove_outputs]
  
          if use_non_granular:
              strategies += [
                  eliminate_dead_code,
                  remove_unused_inputs,
                  consolidate_inputs,
              ]
  
          strategies += [remove_suffix, delta_debugging]
  
          for strategy in strategies:
              new_state = strategy(failing_state, granularity)
              if new_state is not None:
                  return new_state
          return None
  
      while True:
          dump_state(
              fx.GraphModule(fail_f, failing_state.graph),
              failing_state.inps,
          )
          granularity = int(
              2
              ** (math.floor(math.log2(len(failing_state.graph.nodes))))
          )
          if max_granularity is not None:
              granularity = min(max_granularity, granularity)
          new_state = try_granularity(
              failing_state, granularity, use_non_granular=True
          )
          if new_state is not None:
              failing_state = new_state
              continue
  
          granularity //= 2
          has_progress = False
          while granularity >= 1:
              new_state = try_granularity(
                  failing_state, granularity, use_non_granular=False
              )
              if new_state is not None:
                  failing_state = new_state
                  has_progress = True
                  break
              granularity //= 2
          if has_progress:
              continue
  
          new_state = remove_outputs(failing_state, 1)
          if new_state is not None:
              failing_state = new_state
              continue
  
          break
  
      if not graph_fails(failing_state.graph, failing_state.inps):
          raise RuntimeError(
              "Uh oh, something went wrong :( Final graph is not failing"
          )
  
      print(f"Made {num_queries} queries", file=sys.stderr)
      failing_fx = fx.GraphModule(fail_f, failing_state.graph)
  
      # If XLA debugging environment is enabled, create minified HLO graph as well
      if "XLA_HLO_DEBUG" in os.environ:
          create_minified_hlo_graph(failing_fx, failing_state.inps)
  
      dump_state(failing_fx, failing_state.inps)
      print("Wrote minimal repro out to repro.py", file=sys.stderr)
      return failing_fx, failing_state.inps
  
imports: import torch.fx
  import copy
  import torch
  import math
  import sys
  from typing import Callable, List
  from functools import wraps, partial
  from dataclasses import dataclass
  from .compile_utils import get_placeholders, get_outputs
  from torch.utils._content_store import ContentStoreWriter
  from torch.hub import tqdm
  from torch.multiprocessing.reductions import StorageWeakRef
  import os
label_desc: Use image node_id 6 for calling a global function with example usage: minifier(fail_f, inps, module_fails, dump_state) and returns: failing_fx,  failing_state, fx, module_fails, new_func, partial, None, None, None, None, remove_unused_inputs_checked, None, new_graph, None, None, None, None, None, None, ReproState, new_state, ReproState, ReproState, new_state, ReproState, ReproState, new_state, None, ReproState