code: def get_conv_block(
      spatial_dims: int,
      in_channels: int,
      out_channels: int,
      kernel_size: Sequence[int] | int = 3,
      act: tuple | str | None = "RELU",
      norm: tuple | str | None = "BATCH",
  ) -> nn.Module:
      padding = same_padding(kernel_size)
      mod: nn.Module = Convolution(
          spatial_dims,
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          act=act,
          norm=norm,
          bias=False,
          conv_only=False,
          padding=padding,
      )
      return mod
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  from torch import nn
  from torch.nn import functional
  from monai.networks.blocks import Convolution
  from monai.networks.layers import same_padding
  from monai.networks.layers.factories import Conv, Norm, Pool
label_desc: Use image node_id 1 for calling a global function with example usage: get_conv_block(spatial_dims, in_channels, out_channels, kernel_size, act, norm) and returns: mod