class_docstr: Stack Prenet and CBHG module for encoder
  Args:
      inputs (FloatTensor): embedding features
  
  Shapes:
      - inputs: (B, T, D_in)
      - outputs: (B, T, 128 * 2)
class_docstr_tok: ["Stack","Prenet","and","CBHG","module","for","encoder","Args",":","inputs","(","FloatTensor",")",":","embedding","features","Shapes",":","-","inputs",":","(","B",",","T",",","D_in",")","-","outputs",":","(","B",",","T",",","128","*","2",")"]
code: def forward(self, inputs):
      # B x T x prenet_dim
      outputs = self.prenet(inputs)
      outputs = self.cbhg(outputs.transpose(1, 2))
      return outputs
  
imports: import torch
  from torch import nn
  from .attentions import init_attn
  from .common_layers import Prenet
label_desc: Use image node_id 2 for calling the Encoder obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs