class_docstr: Encoder module that transform 1D waveform to 2D representations.
  
  Args:
      feat_dim (int, optional): The feature dimension after Encoder module. (Default: 512)
      win_len (int, optional): kernel size in the Conv1D layer. (Default: 32)
docstr: Apply waveforms to convolutional layer and ReLU layer.
  
  Args:
      x (torch.Tensor): Input waveforms. Tensor with dimensions `(batch, time)`.
  
  Returns:
      (torch,Tensor): Feature Tensor with dimensions `(batch, channel, frame)`.
docstr_tok: ["Apply","waveforms","to","convolutional","layer","and","ReLU","layer",".","Args",":","x","(","torch.Tensor",")",":","Input","waveforms",".","Tensor","with","dimensions","`","(","batch",",","time",")","`",".","Returns",":","(","torch",",","Tensor",")",":","Feature","Tensor","with","dimensions","`","(","batch",",","channel",",","frame",")","`","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Apply waveforms to convolutional layer and ReLU layer.
  
      Args:
          x (torch.Tensor): Input waveforms. Tensor with dimensions `(batch, time)`.
  
      Returns:
          (torch,Tensor): Feature Tensor with dimensions `(batch, channel, frame)`.
      """
      out = x.unsqueeze(dim=1)
      out = F.relu(self.conv1d(out))
      return out
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Encoder obj's underlying member method code with example usage: obj.forward(x) and returns: out