code: def _is_qconfig_supported_by_dtype_configs(
      qconfig: QConfig, dtype_configs: List[DTypeConfig]
  ):
      for dtype_config in dtype_configs:
          is_dynamic = dtype_config.is_dynamic
          if is_dynamic is None:
              is_dynamic = False
          input_dtype = dtype_config.input_dtype or torch.float
          weight_dtype = dtype_config.weight_dtype or torch.float
          bias_dtype = dtype_config.bias_dtype or torch.float
          output_dtype = dtype_config.output_dtype or torch.float
          (
              qconfig_activation_dtype,
              qconfig_weight_dtype,
              qconfig_input_act_is_dynamic,
          ) = get_qconfig_dtypes(qconfig)
          qconfig_bias_dtype = (
              torch.float16
              if (
                  qconfig_activation_dtype == torch.float16
                  and qconfig_weight_dtype == torch.float16
                  and not is_dynamic
              )
              else torch.float
          )
  
          if is_dynamic:
              is_match = (
                  qconfig_input_act_is_dynamic
                  and input_dtype == qconfig_activation_dtype
                  and output_dtype == torch.float
                  and weight_dtype == qconfig_weight_dtype
              )
          else:
              is_match = (
                  input_dtype == qconfig_activation_dtype
                  and output_dtype == qconfig_activation_dtype
                  and weight_dtype == qconfig_weight_dtype
                  and bias_dtype == qconfig_bias_dtype
              )
          if is_match:
              return True
      return False
  
imports: import torch
  import re
  from collections import defaultdict, OrderedDict
  from typing import Callable, Any, Dict, Tuple, Set, List, Union
  from torch.ao.quantization import QConfig
  from torch.ao.quantization.qconfig import _add_module_to_qconfig_obs_ctr, QConfigAny, qconfig_equals
  from torch.ao.quantization.observer import _is_activation_post_process
  from torch.ao.quantization.backend_config import BackendConfig, DTypeConfig
  from torch.ao.quantization.backend_config.utils import get_module_to_qat_module
  from torch.fx import GraphModule
  from torch.fx.graph import Graph
  from torch.ao.nn.intrinsic import _FusedModule
  from ..utils import _parent_name, get_qconfig_dtypes
  from ..qconfig_mapping import _OBJECT_TYPE_DICT_KEY, _MODULE_NAME_DICT_KEY, _MODULE_NAME_REGEX_DICT_KEY, QConfigMapping
label_desc: Use image node_id 6 for calling a global function with example usage: _is_qconfig_supported_by_dtype_configs(qconfig, dtype_configs) and returns: False, True