code: def __init__(
      self,
      spatial_dims: int,
      in_channels: int,
      out_channels: int,
      kernel_size: int = 3,
      strides: int = 1,
      dropout=0.0,
  ):
      super().__init__()
      layers = [
          Convolution(
              spatial_dims=spatial_dims,
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              strides=strides,
              padding=None,
              adn_ordering="NDA",
              act="relu",
              norm=Norm.BATCH,
              dropout=dropout,
          ),
          Convolution(
              spatial_dims=spatial_dims,
              in_channels=out_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              strides=1,
              padding=None,
              adn_ordering="NDA",
              act="relu",
              norm=Norm.BATCH,
              dropout=dropout,
          ),
      ]
      self.conv = nn.Sequential(*layers)
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  import torch.nn
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.layers.factories import Norm
label_desc: Use image node_id 1 to create a new ConvBlock object from inherited base classes: nn with example: obj = ConvBlock(spatial_dims, in_channels, out_channels, kernel_size, strides, dropout)