class_docstr: SGDDecoder
class_docstr_tok: ["SGDDecoder"]
docstr: Obtain logits for status and values for categorical slots
  Slot status values: none, dontcare, active
  Args:
      encoded_utterance: representation of utterance
docstr_tok: ["Obtain","logits","for","status","and","values","for","categorical","slots","Slot","status","values",":","none",",","dontcare",",","active","Args",":","encoded_utterance",":","representation","of","utterance"]
code: def _get_categorical_slot_goals(self, encoded_utterance):
      """
      Obtain logits for status and values for categorical slots
      Slot status values: none, dontcare, active
      Args:
          encoded_utterance: representation of utterance
      """
  
      # Predict the status of all categorical slots.
      status_logits = self.slot_status_layer(
          encoded_utterance=encoded_utterance
      )
  
      value_status_logits = self.cat_slot_value_layer(
          encoded_utterance=encoded_utterance
      )
      return status_logits, value_status_logits
  
file_path: NeMo/nemo/collections/nlp/modules/dialogue_state_tracking/sgd_decoder.py
imports: from typing import Dict, Optional
  import torch
  from torch import nn
  from nemo.core.classes import NeuralModule, typecheck
  from nemo.core.neural_types import LogitsType, NeuralType
label_desc: Use image node_id 6 for calling the SGDDecoder obj's underlying member method code with example usage: obj._get_categorical_slot_goals(encoded_utterance) and returns: status_logits,  value_status_logits