code: def __init__(
      self,
      in_channels=1,
      out_channels=1,
      kernel_size=(3, 1),
      stride=(1, 1),
  ):
      super(ConvLayer, self).__init__()
      self.cnn = nn.Sequential(
          nn.Conv2d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              stride=stride,
          ),
          nn.ReLU(),
          nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.99),
      )
  
imports: from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.exportable import Exportable
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types import EncodedRepresentation, LengthsType, NeuralType, SpectrogramType
  from nemo.core.neural_types.elements import ProbsType
label_desc: Use image node_id 1 to create a new ConvLayer object from inherited base classes: nn with example: obj = ConvLayer(in_channels, out_channels, kernel_size, stride)