class_docstr: Neural module which implements loss for SGD model.
class_docstr_tok: ["Neural","module","which","implements","loss","for","SGD","model","."]
docstr: Returns definitions of module input ports.
  logit_intent_status: Output of SGD model
  intent_status: intent label
  logit_req_slot_status: Output of SGD model
  requested_slot_status: Takes value 1 if the corresponding slot is requested, 0 otherwise
  logit_cat_slot_status: Output of SGD model
  categorical_slot_status: The status of each categorical slot in the service
  logit_cat_slot_value_status: Output of SGD model
  categorical_slot_value_status: Takes value 1 if the corresponding slot value is correct, 0 otherwise
  logit_noncat_slot_status: Output of SGD model
  noncategorical_slot_status: The status of each noncategorical slot in the service            logit_spans: Output of SGD model
  noncategorical_slot_value_start: The index of the starting subword corresponding to the slot span for a non-categorical slot value
  noncategorical_slot_value_end: The index of the ending (inclusive) subword corresponding to the slot span for a non-categorical slot value
  task_mask: Mask contains 1 if its the current task, 0 otherwise
docstr_tok: ["Returns","definitions","of","module","input","ports",".","logit_intent_status",":","Output","of","SGD","model","intent_status",":","intent","label","logit_req_slot_status",":","Output","of","SGD","model","requested_slot_status",":","Takes","value","1","if","the","corresponding","slot","is","requested",",","0","otherwise","logit_cat_slot_status",":","Output","of","SGD","model","categorical_slot_status",":","The","status","of","each","categorical","slot","in","the","service","logit_cat_slot_value_status",":","Output","of","SGD","model","categorical_slot_value_status",":","Takes","value","1","if","the","corresponding","slot","value","is","correct",",","0","otherwise","logit_noncat_slot_status",":","Output","of","SGD","model","noncategorical_slot_status",":","The","status","of","each","noncategorical","slot","in","the","service","logit_spans",":","Output","of","SGD","model","noncategorical_slot_value_start",":","The","index","of","the","starting","subword","corresponding","to","the","slot","span","for","a","non-categorical","slot","value","noncategorical_slot_value_end",":","The","index","of","the","ending","(","inclusive",")","subword","corresponding","to","the","slot","span","for","a","non-categorical","slot","value","task_mask",":","Mask","contains","1","if","its","the","current","task",",","0","otherwise"]
code: def input_types(self):
      """Returns definitions of module input ports.
              logit_intent_status: Output of SGD model
              intent_status: intent label
              logit_req_slot_status: Output of SGD model
              requested_slot_status: Takes value 1 if the corresponding slot is requested, 0 otherwise
              logit_cat_slot_status: Output of SGD model
              categorical_slot_status: The status of each categorical slot in the service
              logit_cat_slot_value_status: Output of SGD model
              categorical_slot_value_status: Takes value 1 if the corresponding slot value is correct, 0 otherwise
              logit_noncat_slot_status: Output of SGD model
              noncategorical_slot_status: The status of each noncategorical slot in the service\
              logit_spans: Output of SGD model
              noncategorical_slot_value_start: The index of the starting subword corresponding to the slot span for a non-categorical slot value
              noncategorical_slot_value_end: The index of the ending (inclusive) subword corresponding to the slot span for a non-categorical slot value
              task_mask: Mask contains 1 if its the current task, 0 otherwise
          """
      return {
          "logit_intent_status": NeuralType(("B", "T"), LogitsType()),
          "intent_status": NeuralType(("B"), LabelsType()),
          "logit_req_slot_status": NeuralType(("B", "T"), LogitsType()),
          "requested_slot_status": NeuralType(("B"), LabelsType()),
          "logit_cat_slot_status": NeuralType(("B", "T"), LogitsType()),
          "categorical_slot_status": NeuralType(("B"), LabelsType()),
          "logit_cat_slot_value_status": NeuralType(
              ("B", "T"), LogitsType()
          ),
          "categorical_slot_value_status": NeuralType(
              ("B"), LabelsType()
          ),
          "logit_noncat_slot_status": NeuralType(
              ("B", "T"), LogitsType()
          ),
          "noncategorical_slot_status": NeuralType(("B"), LabelsType()),
          "logit_spans": NeuralType(("B", "T", "D"), LogitsType()),
          "noncategorical_slot_value_start": NeuralType(
              ("B"), LabelsType()
          ),
          "noncategorical_slot_value_end": NeuralType(
              ("B"), LabelsType()
          ),
          "task_mask": NeuralType(("B", "T"), ChannelType()),
      }
  
file_path: NeMo/nemo/collections/nlp/losses/sgd_loss.py
imports: import torch
  from nemo.core.classes import Loss, Typing, typecheck
  from nemo.core.neural_types import ChannelType, LabelsType, LogitsType, LossType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the SGDDialogueStateLoss obj's underlying member method code with example usage: obj.input_types() and returns: dict