class_docstr: Simple linear projection from the hidden vector to
  vocab.
class_docstr_tok: ["Simple","linear","projection","from","the","hidden","vector","to","vocab","."]
code: def get_probs(
      self, decoder_out: Tuple[Tensor, Dict[str, Tensor]]
  ) -> Tuple[Tensor, Tensor, Tensor]:
      logits, output_dict = decoder_out
      # Any large reductions such as softmax should occur in
      # 32 bits
      probs = F.softmax(logits.float(), dim=-1).to(logits.dtype)
      max_probs, idx = probs.max(dim=-1)
      return idx, max_probs, probs
  
file_path: pytext/pytext/models/seq_models/projection_layers.py
imports: from typing import Dict, Optional, Tuple
  import torch
  import torch.nn.functional
  from pytext.models.seq_models.utils import Linear
  from torch import nn, Tensor
  from .attention import DecoupledMultiheadAttention
  from .utils import verify_encoder_out
label_desc: Use image node_id 4 for calling the DecoderWithLinearOutputProjection obj's underlying member method code with example usage: obj.get_probs(decoder_out) and returns: idx,  max_probs,  probs