code: def transmute_Conv3d5x1x1BnAct(input_module: nn.Module):
      """
      Given an input_module, transmutes it into a equivalent Conv3d5x1x1BnAct.
      Returns None if no equivalent Conv3d5x1x1BnAct is found, else returns
      an instance of equivalent Conv3d5x1x1BnAct.
      Args:
          input_module (nn.Module): input module to find an equivalent Conv3d5x1x1BnAct
      """
      if not isinstance(input_module, nn.Conv3d):
          return None
  
      if (
          input_module.kernel_size == (5, 1, 1)
          and input_module.stride == (1, 1, 1)
          and input_module.padding == (2, 0, 0)
          and input_module.dilation == (1, 1, 1)
          and input_module.padding_mode == "zeros"
      ):
          module = Conv3d5x1x1BnAct(
              in_channels=input_module.in_channels,
              out_channels=input_module.out_channels,
              bias=False if input_module.bias is None else True,
              groups=input_module.groups,
              activation="identity",
              use_bn=False,
          )
          module.kernel.conv.load_state_dict(input_module.state_dict())
          return module
      else:
          return None
  
imports: import torch.nn
  from pytorchvideo.layers.accelerator.mobile_cpu.convolutions import Conv3d3x1x1BnAct, Conv3d3x3x3DwBnAct, Conv3d5x1x1BnAct, Conv3dPwBnAct, Conv3dTemporalKernel1BnAct
label_desc: Use image node_id 5 for calling a global function with example usage: transmute_Conv3d5x1x1BnAct(input_module) and returns: None, module, None