class_docstr: This class follows the structure of TabularNNDataset in tabular_nn_dataset.py,
  
  Class for preprocessing & storing/feeding data batches used by pytorch neural networks for tabular data.
  Assumes entire dataset can be loaded into numpy arrays.
  Original data table may contain numeric and categorical fields and missing values.
  
  Attributes:
      data_list (list[np.array]): Contains the raw data. Different indices in this list correspond to different
                                  types of inputs to the neural network (each is 2D array). All vector-valued
                                  (continuous & one-hot) features are concatenated together into a single index
                                  of the dataset.
      data_desc (list[str]): Describes the data type of each index of dataset
                             (options: 'vector','embed_<featname>')
      embed_indices (list): which columns in dataset correspond to embed features (order matters!)
      vecfeature_col_map (dict): maps vector_feature_name ->  columns of dataset._data[vector] array that
                                 contain the data for this feature
      feature_dataindex_map (dict): maps feature_name -> i such that dataset._data[i] = data array for
                                    this feature. Cannot be used for vector-valued features,
                                    instead use vecfeature_col_map
      feature_groups (dict): maps feature_type (ie. 'vector' or 'embed') to list of feature
                             names of this type (empty list if there are no features of this type)
      vectordata_index (int): describes which element of the dataset._data list holds the vector data matrix
                              (access via self.data_list[self.vectordata_index]); None if no vector features
      label_index (int): describing which element of the dataset._data list holds labels
                         (access via self.data_list[self.label_index]); None if no labels
      num_categories_per_embedfeature (list): Number of categories for each embedding feature (order matters!)
      num_examples (int): number of examples in this dataset
      num_features (int): number of features (we only consider original variables as features, so num_features
                          may not correspond to dimensionality of the data eg in the case of one-hot encoding)
  Note: Default numerical data-type is converted to float32.
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docstr: Returns boolean indicating whether this dataset contains vector features
docstr_tok: ["Returns","boolean","indicating","whether","this","dataset","contains","vector","features"]
code: def has_vector_features(self):
      """Returns boolean indicating whether this dataset contains vector features"""
      return self.vectordata_index is not None
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tabular_nn/torch/tabular_torch_dataset.py
imports: import logging
  import os
  import numpy
  import torch
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
label_desc: Use image node_id 4 for calling the TabularTorchDataset obj's underlying member method code with example usage: obj.has_vector_features() and returns: unknown