docstr: Compile the input model using DeepSparse Compiler.
  
  Args:
      model (torch.nn.Module): The pytorch model.
      onnx_output_path (str): Path where the converted ONNX model will be
          stored.
      model_params (ModelParams): The model parameters.
      quantization_type (QuantizationType): The desired
          quantization algorithm to be used. Default: None.
      input_data (DataManager): User defined data. Default: None
docstr_tok: ["Compile","the","input","model","using","DeepSparse","Compiler",".","Args",":","model","(","torch.nn.Module",")",":","The","pytorch","model",".","onnx_output_path","(","str",")",":","Path","where","the","converted","ONNX","model","will","be","stored",".","model_params","(","ModelParams",")",":","The","model","parameters",".","quantization_type","(","QuantizationType",")",":","The","desired","quantization","algorithm","to","be","used",".","Default",":","None",".","input_data","(","DataManager",")",":","User","defined","data",".","Default",":","None"]
code: def execute(
      self,
      model: Module,
      onnx_output_path: str,
      model_params: ModelParams,
      quantization_type: QuantizationType = None,
      input_data: DataManager = None,
      **kwargs,
  ):
      """Compile the input model using DeepSparse Compiler.
  
      Args:
          model (torch.nn.Module): The pytorch model.
          onnx_output_path (str): Path where the converted ONNX model will be
              stored.
          model_params (ModelParams): The model parameters.
          quantization_type (QuantizationType): 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}."
      )
  
      self.compiled_model = self._compile_model(
          model, onnx_output_path, input_data, model_params
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/deepsparse.py
imports: from pathlib import Path
  from typing import Union
  from nebullvm.core.models import ModelParams, QuantizationType
  from nebullvm.operations.conversions.converters import PytorchConverter
  from nebullvm.operations.optimizations.compilers.base import Compiler
  from nebullvm.optional_modules.torch import Module, GraphModule
  from nebullvm.tools.data import DataManager
label_desc: Use image node_id 2 for calling the DeepSparseCompiler obj's underlying member method code with example usage: obj.execute(model, onnx_output_path, model_params, quantization_type, input_data) without return types