code: def _create_parameters_CONTINUOUS_ACTION(
      self, begin_index: int, norm_params: List[NormalizationParameters]
  ):
      self._create_parameter(
          begin_index,
          "min_serving_value",
          torch.tensor(
              [p.min_value for p in norm_params], device=self.device
          ),
      )
      self._create_parameter(
          begin_index,
          "min_training_value",
          torch.ones(len(norm_params), device=self.device) * -1 + EPS,
      )
      self._create_parameter(
          begin_index,
          "scaling_factor",
          (torch.ones(len(norm_params), device=self.device) - EPS)
          * 2
          / torch.tensor(
              # pyre-fixme[58]: `-` is not supported for operand types
              #  `Optional[float]` and `Optional[float]`.
              [p.max_value - p.min_value for p in norm_params],
              device=self.device,
          ),
      )
  
imports: import logging
  from typing import cast, Dict, List, Optional, Tuple
  import torch
  from reagent.core.parameters import NormalizationParameters
  from reagent.preprocessing.identify_types import DO_NOT_PREPROCESS, ENUM, FEATURE_TYPES
  from reagent.preprocessing.normalization import EPS, MAX_FEATURE_VALUE, MIN_FEATURE_VALUE
  from torch.nn import Module, Parameter
label_desc: Use image node_id 14 for calling the Preprocessor obj's underlying member method code with example usage: obj._create_parameters_CONTINUOUS_ACTION(begin_index, norm_params) without return types