code: def get_conv_layer(
      spatial_dims: int,
      in_channels: int,
      out_channels: int,
      kernel_size: Sequence[int] | int = 3,
      stride: Sequence[int] | int = 1,
      act: tuple | str | None = Act.PRELU,
      norm: tuple | str | None = Norm.INSTANCE,
      dropout: tuple | str | float | None = None,
      bias: bool = False,
      conv_only: bool = True,
      is_transposed: bool = False,
  ):
      padding = get_padding(kernel_size, stride)
      output_padding = None
      if is_transposed:
          output_padding = get_output_padding(
              kernel_size, stride, padding
          )
      return Convolution(
          spatial_dims,
          in_channels,
          out_channels,
          strides=stride,
          kernel_size=kernel_size,
          act=act,
          norm=norm,
          dropout=dropout,
          bias=bias,
          conv_only=conv_only,
          is_transposed=is_transposed,
          padding=padding,
          output_padding=output_padding,
      )
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import numpy
  import torch
  import torch.nn
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.layers.factories import Act, Norm
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
label_desc: Use image node_id 1 for calling a global function with example usage: get_conv_layer(spatial_dims, in_channels, out_channels, kernel_size, stride, act, norm, dropout, bias, conv_only, is_transposed) and returns: Convolution