code: def make_n_channel_input_conv2d_same(
      conv: nn.Conv2d, in_channels: int, mode="auto", **kwargs
  ):
      assert isinstance(conv, nn.Conv2d)
      if conv.in_channels == in_channels:
          warnings.warn("make_n_channel_input call is spurious")
          return conv
  
      from timm.models.layers import Conv2dSame
  
      new_conv = Conv2dSame(
          in_channels,
          out_channels=conv.out_channels,
          kernel_size=kwargs.get("kernel_size", conv.kernel_size),
          stride=kwargs.get("stride", conv.stride),
          padding=kwargs.get("padding", conv.padding),
          dilation=kwargs.get("dilation", conv.dilation),
          groups=kwargs.get("groups", conv.groups),
          bias=kwargs.get("bias", conv.bias is not None),
      )
  
      w = conv.weight
      if in_channels > conv.in_channels:
          n = math.ceil(in_channels / float(conv.in_channels))
          w = torch.cat([w] * n, dim=1)
          w = w[:, :in_channels, ...]
          new_conv.weight = nn.Parameter(w, requires_grad=True)
      else:
          w = w[:, 0:in_channels, ...]
          new_conv.weight = nn.Parameter(w, requires_grad=True)
  
      return new_conv
  
imports: import math
  import warnings
  import torch
  from torch import nn
  from ..common import EncoderModule, _take
  from ...activations import ACT_SILU, get_activation_block
label_desc: Use image node_id 1 for calling a global function with example usage: make_n_channel_input_conv2d_same(conv, in_channels, mode) and returns: new_conv, conv