class_docstr: Progressive Neural Network column.
docstr: :param in_features: size of each input sample
  :param out_features_per_column:
      size of each output sample (single column)
  :param num_prev_modules: number of previous columns
  :param adapter: adapter type. One of {'linear', 'mlp'} (default='mlp')
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code: def __init__(
      self,
      in_features,
      out_features_per_column,
      num_prev_modules,
      adapter="mlp",
  ):
      """
      :param in_features: size of each input sample
      :param out_features_per_column:
          size of each output sample (single column)
      :param num_prev_modules: number of previous columns
      :param adapter: adapter type. One of {'linear', 'mlp'} (default='mlp')
      """
      super().__init__()
      self.in_features = in_features
      self.out_features_per_column = out_features_per_column
      self.num_prev_modules = num_prev_modules
  
      self.itoh = nn.Linear(in_features, out_features_per_column)
      if adapter == "linear":
          self.adapter = LinearAdapter(
              in_features, out_features_per_column, num_prev_modules
          )
      elif adapter == "mlp":
          self.adapter = MLPAdapter(
              in_features, out_features_per_column, num_prev_modules
          )
      else:
          raise ValueError(
              "`adapter` must be one of: {'mlp', `linear'}."
          )
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from avalanche.benchmarks.utils.flat_data import ConstantSequence
  from avalanche.models import MultiTaskModule
  from avalanche.models import MultiHeadClassifier
  from avalanche.benchmarks.scenarios import CLExperience
label_desc: Use image node_id 1 to create a new PNNColumn object from inherited base classes: nn with example: obj = PNNColumn(in_features, out_features_per_column, num_prev_modules, adapter)