docstr: Change number of channels expected in the input tensor. By default,
  all encoders assume 3-channel image in BCHW notation with C=3.
  This method changes first convolution to have user-defined number of
  channels as input.
docstr_tok: ["Change","number","of","channels","expected","in","the","input","tensor",".","By","default",",","all","encoders","assume","3-channel","image","in","BCHW","notation","with","C=3",".","This","method","changes","first","convolution","to","have","user-defined","number","of","channels","as","input","."]
code: def change_input_channels(
      self, input_channels: int, mode="auto", **kwargs
  ):
      """
      Change number of channels expected in the input tensor. By default,
      all encoders assume 3-channel image in BCHW notation with C=3.
      This method changes first convolution to have user-defined number of
      channels as input.
      """
      raise NotImplementedError
  
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 6 for calling the EncoderModule obj's underlying member method code with example usage: obj.change_input_channels(input_channels, mode) without return types