class_docstr: convolutional generator for UASR.
class_docstr_tok: ["convolutional","generator","for","UASR","."]
code: def forward(
      self,
      feats: torch.Tensor,
      text: Optional[torch.Tensor],
      feats_padding_mask: torch.Tensor,
  ):
      inter_x = None
      if self.batch_norm:
          feats = self.bn_padded_data(feats, feats_padding_mask)
      if self.residual:
          inter_x = self.in_proj(self.dropout(feats))
          feats = feats + inter_x
  
      feats = self.dropout(feats)
  
      generated_sample = self.proj(feats)
      generated_sample_padding_mask = feats_padding_mask[
          :, :: self.conv_stride
      ]
  
      if generated_sample_padding_mask.size(1) != generated_sample.size(
          1
      ):
          new_padding = generated_sample_padding_mask.new_zeros(
              generated_sample.shape[:-1]
          )
          diff = new_padding.size(
              1
          ) - generated_sample_padding_mask.size(1)
  
          if diff > 0:
              new_padding[:, diff:] = generated_sample_padding_mask
          else:
              logging.info(
                  "ATTENTION: make sure that you are using V2 instead of V1"
              )
              assert diff < 0
              new_padding = generated_sample_padding_mask[:, :diff]
  
          generated_sample_padding_mask = new_padding
  
      real_sample = None
      if text is not None:
          assert torch.count_nonzero(text) > 0
          real_sample = generated_sample.new_zeros(
              text.numel(), self.output_dim
          )
          real_sample.scatter_(1, text.view(-1, 1).long(), 1)
          real_sample = real_sample.view(
              text.shape + (self.output_dim,)
          )
  
      return (
          generated_sample,
          real_sample,
          inter_x,
          generated_sample_padding_mask,
      )
  
imports: import argparse
  import logging
  from typing import Dict, Optional
  import torch
  from typeguard import check_argument_types
  from espnet2.uasr.generator.abs_generator import AbsGenerator
  from espnet2.utils.types import str2bool
label_desc: Use image node_id 3 for calling the ConvGenerator obj's underlying member method code with example usage: obj.forward(feats, text, feats_padding_mask) and returns: generated_sample,  real_sample,  inter_x,  generated_sample_padding_mask