code: def truncate(inputs, channels):
      input_channels = inputs.size(1)
      if input_channels < channels:
          raise ValueError(
              "input channel < output channels for truncate"
          )
      elif input_channels == channels:
          return inputs  # No truncation necessary
      else:
          # Truncation should only be necessary when channel division leads to
          # vertices with +1 channels. The input vertex should always be projected to
          # the minimum channel count.
          assert input_channels - channels == 1
          return inputs[:, :channels]
  
imports: import logging
  from collections import OrderedDict
  from typing import Callable, List, Optional, Union, Dict, Tuple, Callable, Iterable, Any, cast
  import numpy
  import torch
  import torch.nn
  from nni.mutable import Constraint, Categorical, CategoricalMultiple, Constraint, ConstraintViolation, Mutable, LabeledMutable, Sample, SampleValidationError, auto_label, label_scope
  from nni.mutable.mutable import _mutable_equal
  from nni.nas.nn.pytorch import InputChoice, LayerChoice, MutableModule
label_desc: Use image node_id 3 for calling a global function with example usage: truncate(inputs, channels) and returns: inputs, inputs