original_docstring: 
          A Estimator fit() call to initiate the first model of the experiment
          
docstring: A Estimator fit() call to initiate the first model of the experiment
docstring_tokens: A Estimator fit () call to initiate the first model of the experiment
code: def _fit_first_model(
          self, input_data_s3_prefix=None, manifest_file_path=None, wait=False, logs=True
      ):
          """
          A Estimator fit() call to initiate the first model of the experiment
          """
          rl_estimator_args = self._get_rl_estimator_args()
          self.rl_estimator = RLEstimator(**rl_estimator_args)
  
          if manifest_file_path:
              input_data = sagemaker.inputs.TrainingInput(
                  s3_data=manifest_file_path, input_mode="File", s3_data_type="ManifestFile"
              )
              self.rl_estimator.fit(job_name=self.model_id, inputs=input_data, wait=wait, logs=logs)
          else:
              self.rl_estimator.fit(
                  job_name=self.model_id, inputs=input_data_s3_prefix, wait=wait, logs=logs
              )
code_tokens: def _fit_first_model ( self , input_data_s3_prefix = None , manifest_file_path = None , wait = False , logs = True ) : rl_estimator_args = self . _get_rl_estimator_args ( ) self . rl_estimator = RLEstimator ( ** rl_estimator_args ) if manifest_file_path : input_data = sagemaker . inputs . TrainingInput ( s3_data = manifest_file_path , input_mode = "File" , s3_data_type = "ManifestFile" ) self . rl_estimator . fit ( job_name = self . model_id , inputs = input_data , wait = wait , logs = logs ) else : self . rl_estimator . fit ( job_name = self . model_id , inputs = input_data_s3_prefix , wait = wait , logs = logs )
short_docstring: A Estimator fit() call to initiate the first model of the experiment
short_docstring_tokens: A Estimator fit () call to initiate the first model of the experiment
comment: """
          A Estimator fit() call to initiate the first model of the experiment
          """