class_docstr: Custom configuration for :func:`~torch.ao.quantization.quantize_fx.prepare_fx` and
  :func:`~torch.ao.quantization.quantize_fx.prepare_qat_fx`.
  
  Example usage::
  
      prepare_custom_config = PrepareCustomConfig()             .set_standalone_module_name("module1", qconfig_mapping, example_inputs,                 child_prepare_custom_config, backend_config)             .set_standalone_module_class(MyStandaloneModule, qconfig_mapping, example_inputs,                 child_prepare_custom_config, backend_config)             .set_float_to_observed_mapping(FloatCustomModule, ObservedCustomModule)             .set_non_traceable_module_names(["module2", "module3"])             .set_non_traceable_module_classes([NonTraceableModule1, NonTraceableModule2])             .set_input_quantized_indexes([0])             .set_output_quantized_indexes([0])             .set_preserved_attributes(["attr1", "attr2"])
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docstr: Set the indexes of the inputs of the graph that should be quantized.
  Inputs are otherwise assumed to be in fp32 by default instead.
docstr_tok: ["Set","the","indexes","of","the","inputs","of","the","graph","that","should","be","quantized",".","Inputs","are","otherwise","assumed","to","be","in","fp32","by","default","instead","."]
code: def set_input_quantized_indexes(
      self, indexes: List[int]
  ) -> PrepareCustomConfig:
      """
      Set the indexes of the inputs of the graph that should be quantized.
      Inputs are otherwise assumed to be in fp32 by default instead.
      """
      self.input_quantized_indexes = indexes
      return self
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Type
  from torch.ao.quantization import QConfigMapping
  from torch.ao.quantization.backend_config import BackendConfig
  from torch.ao.quantization.quant_type import QuantType, _quant_type_from_str, _get_quant_type_to_str
label_desc: Use image node_id 8 for calling the PrepareCustomConfig obj's underlying member method code with example usage: obj.set_input_quantized_indexes(indexes) and returns: self