code: def get_probs(
      self, decoder_out: Tuple[Tensor, Dict[str, Tensor]]
  ) -> Tuple[Tensor, Tensor, Tensor]:
      return self.projection_layer.get_probs(decoder_out)
  
imports: import math
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.config import ConfigBase
  from pytext.config.module_config import ModuleConfig
  from pytext.models.module import create_module
  from pytext.models.seq_models.base import PlaceholderAttentionIdentity
  from pytext.models.seq_models.positional import build_positional_embedding, PostionalEmbedCombine, PostionalEmbedType
  from pytext.models.seq_models.utils import Linear, log_and_overwrite
  from torch import Tensor
  from torch.nn import LayerNorm
  from .attention import MultiheadAttention
  from .base import PlaceholderIdentity, PyTextIncrementalDecoderComponent, PyTextSeq2SeqModule
  from .light_conv import LightweightConv
  from .projection_layers import DecoderWithLinearOutputProjection, DecoupledDecoderHead
  from .utils import extract_ontology_vocab
label_desc: Use image node_id 3 for calling the LightConvDecoder obj's underlying member method code with example usage: obj.get_probs(decoder_out) and returns: self