code: def __init__(
      self,
      spatial_dims: int,
      num_input_features: int,
      growth_rate: int,
      bn_size: int,
      dropout_prob: float,
  ):
      super().__init__()
      # 1x1x1
  
      conv_type = Conv[Conv.CONV, spatial_dims]
      norm_type: type[nn.BatchNorm2d | nn.BatchNorm3d] = Norm[
          Norm.BATCH, spatial_dims
      ]
      relu_type: type[nn.ReLU] = Act[Act.RELU]
  
      self.bn1 = norm_type(num_input_features)
      self.relu1 = relu_type(inplace=True)
      self.conv1 = conv_type(
          num_input_features,
          bn_size * growth_rate,
          kernel_size=1,
          stride=1,
          bias=False,
      )
      # 3x3x1
      self.bn2 = norm_type(bn_size * growth_rate)
      self.relu2 = relu_type(inplace=True)
      self.conv2 = conv_type(
          bn_size * growth_rate,
          growth_rate,
          kernel_size=(3, 3, 1)[-spatial_dims:],
          stride=1,
          padding=(1, 1, 0)[-spatial_dims:],
          bias=False,
      )
      # 1x1x3
      self.bn3 = norm_type(growth_rate)
      self.relu3 = relu_type(inplace=True)
      self.conv3 = conv_type(
          growth_rate,
          growth_rate,
          kernel_size=(1, 1, 3)[-spatial_dims:],
          stride=1,
          padding=(0, 0, 1)[-spatial_dims:],
          bias=False,
      )
      # 1x1x1
      self.bn4 = norm_type(growth_rate)
      self.relu4 = relu_type(inplace=True)
      self.conv4 = conv_type(
          growth_rate, growth_rate, kernel_size=1, stride=1, bias=False
      )
      self.dropout_prob = dropout_prob
  
imports: from __future__ import annotations
  import math
  from collections.abc import Sequence
  from typing import Union
  import torch
  import torch.nn
  import torch.nn.functional
  from monai.networks.blocks.fcn import FCN
  from monai.networks.layers.factories import Act, Conv, Norm, Pool
label_desc: Use image node_id 1 to create a new Pseudo3DLayer object from inherited base classes: nn with example: obj = Pseudo3DLayer(spatial_dims, num_input_features, growth_rate, bn_size, dropout_prob)