code: def __init__(
      self,
      spatial_dims: int,
      in_channels: int,
      out_channels: int,
      kernel_size=3,
      strides=2,
      dropout=0.0,
  ):
      super().__init__()
      self.up = Convolution(
          spatial_dims,
          in_channels,
          out_channels,
          strides=strides,
          kernel_size=kernel_size,
          act="relu",
          adn_ordering="NDA",
          norm=Norm.BATCH,
          dropout=dropout,
          is_transposed=True,
      )
  
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 UpConv object from inherited base classes: nn with example: obj = UpConv(spatial_dims, in_channels, out_channels, kernel_size, strides, dropout)