code: def _qconfig_satisfies_dtype_config_constraints(
      qconfig: QConfigAny,
      dtype_with_constraints: DTypeWithConstraints,
      is_activation: bool = True,
  ) -> bool:
      """
      Return whether `qconfig` satisfies the following constraints from the backend,
      specified through the activation and weight DTypeWithConstraints.
  
          1. QConfig specified a quantization range that falls within the backend's, if any
          2. QConfig specified a min scale value that is >= the backend's, if any
          3. QConfig specified a FixedQParamsObserver or FixedQParamsFakeQuantize that has
             scale and zero point that match the backend's, if any
  
      If `is_activation` is True, we check `qconfig.activation`, else we check `qconfig.weight`.
      If `qconfig` or `dtype_with_constraints.dtype` is None, or the dtypes do not match, return True.
      """
  
      # TODO: log warnings only when the user enabled a debug flag
      def _activation_post_process_satisfies_dtype_config_constraints(
          activation_post_process: Union[
              ObserverBase, FakeQuantizeBase
          ],
          dtype_with_constraints: DTypeWithConstraints,
          debug_string: str,
      ) -> bool:
          observer = _get_observer_from_activation_post_process(
              activation_post_process
          )
          app_quant_min = getattr(observer, "quant_min", None)
          app_quant_max = getattr(observer, "quant_max", None)
          # TODO: for now, just use the existing eps value as scale_min. In the future, we should
          # resolve the differences between the two, either by renaming eps or some other way
          app_scale_min = getattr(observer, "eps", None)
          backend_quant_min = (
              dtype_with_constraints.quant_min_lower_bound
          )
          backend_quant_max = (
              dtype_with_constraints.quant_max_upper_bound
          )
          backend_scale_min = (
              dtype_with_constraints.scale_min_lower_bound
          )
          backend_scale_exact_match = (
              dtype_with_constraints.scale_exact_match
          )
          backend_zero_point_exact_match = (
              dtype_with_constraints.zero_point_exact_match
          )
          # check quantization ranges
          if (
              backend_quant_min is not None
              and backend_quant_max is not None
          ):
              if app_quant_min is None or app_quant_max is None:
                  warnings.warn(
                      f"QConfig {debug_string} must specify 'quant_min' and 'quant_max', ignoring {qconfig}"
                  )
                  return False
              elif (
                  app_quant_min < backend_quant_min
                  or app_quant_max > backend_quant_max
              ):
                  warnings.warn(
                      f"QConfig {debug_string} quantization range must fall within the backend's:\n"
                      f"QConfig range = ({app_quant_min}, {app_quant_max}), "
                      f"BackendConfig range = ({backend_quant_min}, {backend_quant_max}), "
                      f"ignoring {qconfig}"
                  )
                  return False
          # check scale min
          if backend_scale_min is not None:
              if app_scale_min is None:
                  warnings.warn(
                      f"QConfig {debug_string} must specify 'eps', ignoring {qconfig}"
                  )
                  return False
              if app_scale_min < backend_scale_min:
                  warnings.warn(
                      f"QConfig {debug_string} eps ({app_scale_min}) must be greater than or equal to "
                      f"the backend's min scale value ({backend_scale_min}), ignoring {qconfig}"
                  )
                  return False
          # check fixed scale and zero point
          if (
              backend_scale_exact_match is not None
              and backend_zero_point_exact_match is not None
          ):
              # For tests only, accept the following qconfigs for now
              # TODO: handle fp16 qconfigs properly
              for accepted_qconfig in [
                  float16_static_qconfig,
                  float16_dynamic_qconfig,
              ]:
                  if qconfig_equals(qconfig, accepted_qconfig):
                      return True
              suggestion_str = (
                  "Please use torch.ao.quantization.get_default_qconfig_mapping or "
                  "torch.ao.quantization.get_default_qat_qconfig_mapping. Example:\n"
                  '    qconfig_mapping = get_default_qconfig_mapping("fbgemm")\n'
                  "    model = prepare_fx(model, qconfig_mapping, example_inputs)"
              )
              if not isinstance(
                  activation_post_process, FixedQParamsObserver
              ) and not isinstance(
                  activation_post_process, FixedQParamsFakeQuantize
              ):
                  warnings.warn(
                      f"QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
                      f"for fixed qparams ops, ignoring {qconfig}.\n{suggestion_str}"
                  )
                  return False
              if (
                  observer.scale != backend_scale_exact_match
                  or observer.zero_point
                  != backend_zero_point_exact_match
              ):
                  warnings.warn(
                      f"QConfig fixed scale ({observer.scale}) and zero point ({observer.zero_point}) "
                      f"do not match the backend's ({backend_scale_exact_match} and {backend_zero_point_exact_match}), "
                      f"ignoring {qconfig}.\n{suggestion_str}"
                  )
                  return False
          return True
  
      if qconfig is None or dtype_with_constraints.dtype is None:
          return True
  
      activation_post_process_ctr = (
          qconfig.activation if is_activation else qconfig.weight
      )
      debug_string = "activation" if is_activation else "weight"
      satisfies_constraints = True
      if activation_post_process_ctr is not None:
          activation_post_process = activation_post_process_ctr()
          assert _is_activation_post_process(activation_post_process)
          # If dtypes don't match, don't check the activation_post_process and return True early
          if (
              activation_post_process.dtype
              != dtype_with_constraints.dtype
          ):
              return True
          satisfies_constraints = _activation_post_process_satisfies_dtype_config_constraints(
              activation_post_process,
              dtype_with_constraints,
              debug_string,
          )
      return satisfies_constraints
  
imports: import copy
  import torch
  import torch.nn
  from torch.ao.quantization import QConfigAny, QuantType
  from torch.ao.quantization.backend_config import DTypeWithConstraints
  from torch.ao.quantization.fake_quantize import FakeQuantizeBase, FixedQParamsFakeQuantize
  from torch.ao.quantization.observer import FixedQParamsObserver, ObserverBase
  from torch.ao.quantization.qconfig import float16_static_qconfig, float16_dynamic_qconfig, qconfig_equals
  from torch.ao.quantization.stubs import DeQuantStub
  from torch.ao.quantization.utils import activation_is_statically_quantized
  from torch.ao.quantization.observer import _is_activation_post_process
  from torch.ao.quantization.qconfig_mapping import QConfigMapping
  from torch.fx import GraphModule, map_arg
  from torch.fx.graph import Graph, Node
  from .custom_config import PrepareCustomConfig
  from ._decomposed import quantized_decomposed_lib
  from typing import Callable, Optional, List, Dict, Any, Set, Tuple, Union, Type
  from dataclasses import dataclass
  from collections import namedtuple
  import operator
  import warnings
label_desc: Use image node_id 26 for calling a global function with example usage: _qconfig_satisfies_dtype_config_constraints(qconfig, dtype_with_constraints, is_activation) and returns: satisfies_constraints, True, True, True, False, False, False, False, False, False, True