docstr: [This property is used to return the number of trainable parameters for a given layer]
  It is useful for debugging and reproducibility.
  Returns:
      [type] -- [description]
docstr_tok: ["[","This","property","is","used","to","return","the","number","of","trainable","parameters","for","a","given","layer","]","It","is","useful","for","debugging","and","reproducibility",".","Returns",":","[","type","]","--","[","description","]"]
code: def nb_params(self):
      """[This property is used to return the number of trainable parameters for a given layer]
      It is useful for debugging and reproducibility.
      Returns:
          [type] -- [description]
      """
      model_parameters = filter(
          lambda p: p.requires_grad, self.parameters()
      )
      self._nb_params = sum(
          [np.prod(p.size()) for p in model_parameters]
      )
      return self._nb_params
  
imports: import numpy
  import torch
  from torch.nn import Linear, ReLU, LeakyReLU, BatchNorm1d, Dropout
  from torch_geometric.nn import knn_interpolate, fps, radius, global_max_pool, global_mean_pool, knn
  from torch_geometric.data import Data
  import torch_points_kernels
  from torch_points3d.core.spatial_ops import BaseMSNeighbourFinder
  from torch_points3d.core.base_conv import BaseConvolution
  from torch_points3d.core.common_modules.dense_modules import MLP2D
  from torch_points3d.utils.enums import ConvolutionFormat
  from torch_points3d.utils.model_building_utils.activation_resolver import get_activation
label_desc: Use image node_id 2 for calling the GlobalDenseBaseModule obj's underlying member method code with example usage: obj.nb_params() and returns: self