code: def _register_qconv_weight_prepack():
      for dtype in [torch.float32, torch.bfloat16]:
          weight_prepack_patterns = (
              _generate_qconv_weight_prepack_patterns(dtype)
          )
          for weight_prepack_pattern in weight_prepack_patterns:
              # Register to pass_number 1, so we can do dequant promotion in pass_number 0.
              _register_qconv_weight_prepack_pass(
                  weight_prepack_pattern, pass_number=1, dtype=dtype
              )
  
imports: import copy
  import functools
  import itertools
  import math
  import operator
  from typing import Any, Tuple
  import torch
  from torch._dynamo.utils import counters
  from torch.fx.experimental.symbolic_shapes import has_free_symbols
  from ..lowering import lowerings, require_channels_last
  from ..pattern_matcher import Arg, CallFunction, filter_nodes, KeywordArg, ListOf, Match
  from ..utils import pad_listlike
  from .freezing_patterns import register_freezing_graph_pattern
  from .post_grad import register_lowering_pattern
label_desc: Use image node_id 39 for calling a global function with example usage: _register_qconv_weight_prepack() without return types