class_docstr: VGG-like module.
  
  Args:
      in_channel: number of input channels
class_docstr_tok: ["VGG-like","module",".","Args",":","in_channel",":","number","of","input","channels"]
docstr: Initialize VGG-like module.
docstr_tok: ["Initialize","VGG-like","module","."]
code: def __init__(self, in_channel: int = 1):
      """Initialize VGG-like module."""
      super(VGG2L, self).__init__()
  
      # CNN layer (VGG motivated)
      self.conv1_1 = torch.nn.Conv2d(
          in_channel, 64, 3, stride=1, padding=1
      )
      self.conv1_2 = torch.nn.Conv2d(64, 64, 3, stride=1, padding=1)
      self.conv2_1 = torch.nn.Conv2d(64, 128, 3, stride=1, padding=1)
      self.conv2_2 = torch.nn.Conv2d(128, 128, 3, stride=1, padding=1)
  
      self.in_channel = in_channel
  
imports: from argparse import Namespace
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from espnet.nets.e2e_asr_common import get_vgg2l_odim
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask, to_device
label_desc: Use image node_id 1 to create a new VGG2L object from inherited base classes: torch.nn with example: obj = VGG2L(in_channel)