docstr: Optimize the input model using pytorch built-in techniques.
  
  Args:
      model (torch.nn.Module): The pytorch model.
      input_tfms (MultiStageTransformation, optional): Transformations
          to be performed to the model's input tensors in order to
          get the prediction. Default: None.
      metric_drop_ths (float, optional): Threshold for the accepted drop
          in terms of precision. Any optimized model with a higher drop
          will be ignored. Default: None.
      quantization_type (QuantizationType, optional): The desired
          quantization algorithm to be used. Default: None.
      input_data (DataManager): User defined data. Default: None.
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code: def execute(
      self,
      model: Module,
      input_tfms: MultiStageTransformation = None,
      metric_drop_ths: float = None,
      quantization_type: QuantizationType = None,
      input_data: DataManager = None,
      **kwargs,
  ):
      """Optimize the input model using pytorch built-in techniques.
  
      Args:
          model (torch.nn.Module): The pytorch model.
          input_tfms (MultiStageTransformation, optional): Transformations
              to be performed to the model's input tensors in order to
              get the prediction. Default: None.
          metric_drop_ths (float, optional): Threshold for the accepted drop
              in terms of precision. Any optimized model with a higher drop
              will be ignored. Default: None.
          quantization_type (QuantizationType, optional): The desired
              quantization algorithm to be used. Default: None.
          input_data (DataManager): User defined data. Default: None.
      """
  
      if (
          quantization_type
          not in self.supported_ops[self.device.type.value]
      ):
          self.compiled_model = None
          return
  
      if (
          quantization_type is QuantizationType.STATIC
          and input_data is None
      ):
          raise ValueError(
              "Input data is required for static quantization."
          )
  
      self.logger.info(
          f"Optimizing with {self.__class__.__name__} and "
          f"q_type: {quantization_type}."
      )
  
      check_quantization(quantization_type, metric_drop_ths)
      train_input_data = input_data.get_split("train").get_list(
          QUANTIZATION_DATA_NUM
      )
  
      if quantization_type is not None:
          model = self._quantize_model(
              model, quantization_type, input_tfms, train_input_data
          )
  
      self.compiled_model = self._compile_model(
          model, input_data, quantization_type
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/torchscript.py
imports: from typing import Union, List, Tuple
  from nebullvm.config import QUANTIZATION_DATA_NUM
  from nebullvm.core.models import QuantizationType, DeviceType
  from nebullvm.operations.optimizations.compilers.base import Compiler
  from nebullvm.operations.optimizations.compilers.quantizations.pytorch import quantize_pytorch
  from nebullvm.operations.optimizations.compilers.quantizations.utils import check_quantization
  from nebullvm.optional_modules.torch import torch, Module, ScriptModule, GraphModule, symbolic_trace
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 1 for calling the TorchScriptCompiler obj's underlying member method code with example usage: obj.execute(model, input_tfms, metric_drop_ths, quantization_type, input_data) without return types