code: def n_layer(self) -> int:
      return len(self.blocks)
  
imports: from typing import Optional, List, Tuple, Union, Iterator, Callable
  from itertools import product, chain
  from copy import deepcopy
  import warnings
  import math
  import torch
  from torch import Tensor, LongTensor, BoolTensor
  from torch import nn
  from torch.nn import Parameter, functional
  from tslib.nn.activations import PositiveSoftplus
  from ..network import AttentionKernel, ExpKernel, RBFKernel, EncoderModule, DecoderModule, AttentionBlock, AdversarialBlock, SimpleDiscriminator
  from .hooks import ZScoreNormalizer, LossFunction, MSELoss, MAELoss
label_desc: Use image node_id 4 for calling the AttentionEstimator obj's underlying member method code with example usage: obj.n_layer() and returns: len