code: def initialize_parameters(self, input) -> None:  # type: ignore[override]
      if self.has_uninitialized_params():
          self.num_features = input.shape[1]
          if self.affine:
              assert isinstance(self.weight, UninitializedParameter)
              assert isinstance(self.bias, UninitializedParameter)
              self.weight.materialize((self.num_features,))
              self.bias.materialize((self.num_features,))
          if self.track_running_stats:
              self.running_mean.materialize(
                  (self.num_features,)
              )  # type:ignore[union-attr]
              self.running_var.materialize(
                  (self.num_features,)
              )  # type:ignore[union-attr]
          self.reset_parameters()
  
imports: from typing import Optional, Any
  import torch
  from torch import Tensor
  from torch.nn.parameter import Parameter, UninitializedParameter, UninitializedBuffer
  from ..None import functional
  from ..None import init
  from ._functions import SyncBatchNorm
  from .lazy import LazyModuleMixin
  from .module import Module
label_desc: Use image node_id 3 for calling the _LazyNormBase obj's underlying member method code with example usage: obj.initialize_parameters(input) without return types