class_docstr: Linear decoder for speaker diarization
docstr: Forward.
  Args:
      input (torch.Tensor): hidden_space [Batch, T, F]
      ilens (torch.Tensor): input lengths [Batch]
docstr_tok: ["Forward",".","Args",":","input","(","torch.Tensor",")",":","hidden_space","[","Batch",",","T",",","F","]","ilens","(","torch.Tensor",")",":","input","lengths","[","Batch","]"]
code: def forward(self, input: torch.Tensor, ilens: Optional[torch.Tensor]):
      """Forward.
      Args:
          input (torch.Tensor): hidden_space [Batch, T, F]
          ilens (torch.Tensor): input lengths [Batch]
      """
      # TODO2 (checkpoint3): compute mean over time-domain (dimension 1)
  
      # TODO3 (checkpoint3): apply the projection layer
  
      # TODO4 (checkpoint3): change the return value
      return None
  
imports: from typing import Optional
  import torch
  from espnet2.asvspoof.decoder.abs_decoder import AbsDecoder
label_desc: Use image node_id 2 for calling the LinearDecoder obj's underlying member method code with example usage: obj.forward(input, ilens) and returns: None