class_docstr: Class for TabNet model.
class_docstr_tok: ["Class","for","TabNet","model","."]
docstr: Compute global feature importance.
  
  Parameters
  ----------
  loader : `torch.utils.data.Dataloader`
      Pytorch dataloader.
docstr_tok: ["Compute","global","feature","importance",".","Parameters","--","--","--","--","--","loader",":","`","torch.utils.data.Dataloader","`","Pytorch","dataloader","."]
code: def _compute_feature_importances(self, X):
      """Compute global feature importance.
  
      Parameters
      ----------
      loader : `torch.utils.data.Dataloader`
          Pytorch dataloader.
  
      """
      M_explain, _ = self.explain(X, normalize=False)
      sum_explain = M_explain.sum(axis=0)
      feature_importances_ = sum_explain / np.sum(sum_explain)
      return feature_importances_
  
imports: from dataclasses import dataclass, field
  from typing import List, Any, Dict
  import torch
  from torch.nn.utils import clip_grad_norm_
  import numpy
  from scipy.sparse import csc_matrix
  from abc import abstractmethod
  from pytorch_tabnet import tab_network
  from pytorch_tabnet.utils import SparsePredictDataset, PredictDataset, create_explain_matrix, validate_eval_set, create_dataloaders, define_device, ComplexEncoder, check_input, check_warm_start, create_group_matrix, check_embedding_parameters
  from pytorch_tabnet.callbacks import CallbackContainer, History, EarlyStopping, LRSchedulerCallback
  from pytorch_tabnet.metrics import MetricContainer, check_metrics
  from sklearn.base import BaseEstimator
  from torch.utils.data import DataLoader
  import io
  import json
  from pathlib import Path
  import shutil
  import zipfile
  import warnings
  import copy
  import scipy
label_desc: Use image node_id 19 for calling the TabModel obj's underlying member method code with example usage: obj._compute_feature_importances(X) and returns: feature_importances_