code: def make_n_channel_input_conv(
      conv: Union[nn.Conv1d, nn.Conv2d, nn.Conv3d],
      in_channels: int,
      mode="auto",
      **kwargs,
  ) -> Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]:
      """
      Create convolution block with same parameters and desired number of channels.
  
      Args:
          conv: Input nn.Conv2D object to copy settings/weights from
          in_channels: Desired number of input channels
          mode:
          **kwargs: Optional overrides for Conv2D parameters
      """
      conv_cls = conv.__class__
  
      if conv.in_channels == in_channels:
          warnings.warn("make_n_channel_input call is spurious")
          return conv
  
      new_conv = conv_cls(
          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),
          padding_mode=kwargs.get("padding_mode", conv.padding_mode),
      )
  
      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
  from typing import List, Union, Tuple, Iterable, Any
  import torch
  from torch import nn, Tensor
  from pytorch_toolbelt.utils.support import pytorch_toolbelt_deprecated
  from pytorch_toolbelt.modules.interfaces import HasOutputFeaturesSpecification, FeatureMapsSpecification, AbstractEncoder
label_desc: Use image node_id 4 for calling a global function with example usage: make_n_channel_input_conv(conv, in_channels, mode) and returns: new_conv, conv