docstr: returns prediction probabilities
  
  :param input_tensor: test input.
  :param transform_data: boolean to indicate if data transformation is required.
docstr_tok: ["returns","prediction","probabilities",":","param","input_tensor",":","test","input",".",":","param","transform_data",":","boolean","to","indicate","if","data","transformation","is","required","."]
code: def get_output(
      self,
      input_instance,
      model_score=True,
      transform_data=False,
      out_tensor=False,
  ):
      """returns prediction probabilities
  
      :param input_tensor: test input.
      :param transform_data: boolean to indicate if data transformation is required.
      """
      input_tensor = input_instance
      if transform_data:
          input_tensor = torch.tensor(
              self.transformer.transform(input_instance).to_numpy()
          ).float()
      if not torch.is_tensor(input_instance):
          input_tensor = torch.tensor(
              self.transformer.transform(input_instance).to_numpy()
          ).float()
      out = self.model(input_tensor).float()
      if not out_tensor:
          out = out.data.numpy()
      if (
          model_score is False
          and self.model_type == ModelTypes.Classifier
      ):
          out = np.round(
              out
          )  # TODO need to generalize for n-class classifier
      return out
  
file_path: DiCE/dice_ml/model_interfaces/pytorch_model.py
imports: import numpy
  import torch
  from dice_ml.constants import ModelTypes
  from dice_ml.model_interfaces.base_model import BaseModel
label_desc: Use image node_id 3 for calling the PyTorchModel obj's underlying member method code with example usage: obj.get_output(input_instance, model_score, transform_data, out_tensor) and returns: out