class_docstr: Similar to Conv2dSubsampling module, but without any subsampling performed.
  
  Args:
      idim (int): Input dimension.
      odim (int): Output dimension.
      dropout_rate (float): Dropout rate.
      pos_enc (torch.nn.Module): Custom position encoding layer.
class_docstr_tok: ["Similar","to","Conv2dSubsampling","module",",","but","without","any","subsampling","performed",".","Args",":","idim","(","int",")",":","Input","dimension",".","odim","(","int",")",":","Output","dimension",".","dropout_rate","(","float",")",":","Dropout","rate",".","pos_enc","(","torch.nn.Module",")",":","Custom","position","encoding","layer","."]
docstr: Get item.
  
  When reset_parameters() is called, if use_scaled_pos_enc is used,
      return the positioning encoding.
code: def __getitem__(self, key):
      """Get item.
  
      When reset_parameters() is called, if use_scaled_pos_enc is used,
          return the positioning encoding.
  
      """
      if key != -1:
          raise NotImplementedError(
              "Support only `-1` (for `reset_parameters`)."
          )
      return self.out[key]
  
imports: import torch
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
label_desc: Use image node_id 3 for calling the Conv2dSubsampling1 obj's underlying member method code with example usage: obj.__getitem__(key) and returns: self