class_docstr: SGDDecoder
class_docstr_tok: ["SGDDecoder"]
docstr: Get logits for elements by conditioning on utterance embedding.
  
  Args:
      embedding_dim: hidden size of the BERT
docstr_tok: ["Get","logits","for","elements","by","conditioning","on","utterance","embedding",".","Args",":","embedding_dim",":","hidden","size","of","the","BERT"]
code: def __init__(self, embedding_dim: int) -> None:
      """Get logits for elements by conditioning on utterance embedding.
  
      Args:
          embedding_dim: hidden size of the BERT
      """
      super().__init__()
  
      projection_module = LogitsQA
  
      self.intent_layer = projection_module(1, embedding_dim)
      self.requested_slots_layer = projection_module(1, embedding_dim)
  
      self.cat_slot_value_layer = projection_module(1, embedding_dim)
  
      # Slot status values: none, dontcare, active.
      self.slot_status_layer = projection_module(3, embedding_dim)
  
      # dim 2 for non_categorical slot - to represent start and end position
      self.noncat_layer1 = nn.Linear(embedding_dim, embedding_dim)
      self.noncat_activation = nn.functional.gelu
      self.noncat_layer2 = nn.Linear(embedding_dim, 2)
  
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 2 to create a new SGDDecoder object from inherited base classes: NeuralModule with example: obj = SGDDecoder(embedding_dim)