code: def get_dynamic_quant_module_class(
      float_module_class: Callable,
      additional_dynamic_quant_mapping: Optional[
          Dict[Callable, Any]
      ] = None,
  ) -> Any:
      r"""n Get the dynamically quantized module class corresponding to
      the floating point module class
      """
      if additional_dynamic_quant_mapping is None:
          additional_dynamic_quant_mapping = {}
      all_mappings = get_combined_dict(
          DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS,
          additional_dynamic_quant_mapping,
      )
      dynamic_quant_module_class = all_mappings.get(
          float_module_class, None
      )
      assert dynamic_quant_module_class is not None, (
          f"Floating point module class {str(float_module_class)}"
          + " does not have a corresponding quantized module class"
      )
      return copy.deepcopy(dynamic_quant_module_class)
  
imports: import copy
  import torch
  from torch import nn
  import torch.nn.functional
  import torch.ao.nn.intrinsic
  import torch.ao.nn.intrinsic.quantized
  import torch.ao.nn.intrinsic.quantized.dynamic
  import torch.ao.nn.intrinsic.qat
  import torch.ao.nn.quantized
  import torch.ao.nn.quantized.reference
  import torch.ao.nn.quantized.dynamic
  import torch.ao.nn.qat
  import torch.ao.nn.qat.dynamic
  from typing import Optional, Union, Dict, Set, Callable, Any
  import torch.ao.nn.sparse
  import torch.ao.nn
  from torch.ao.quantization.stubs import QuantStub, DeQuantStub
  from torch.ao.quantization.fake_quantize import default_fixed_qparams_range_0to1_fake_quant, default_fixed_qparams_range_neg1to1_fake_quant
  from torch.ao.quantization.utils import get_combined_dict
  from torch.nn.utils.parametrize import type_before_parametrizations
label_desc: Use image node_id 7 for calling a global function with example usage: get_dynamic_quant_module_class(float_module_class, additional_dynamic_quant_mapping) and returns: copy