class_docstr: Base class that holds necessary (minimal) information needed
docstr: Initialize parameters
  
  Args:
      name (str): Name of the dataset
      features_dim (uint): Dimension of the features. If not 0, PCA is performed
          on the features as the dimensionality reduction technique
      normalize (bool): Normalize the features
      target_transform (str): Transform the target values. Current options are
          'normalize' (Normalize the values), '' (Do nothing), 'binarize' (convert
          the values using a threshold defined per dataset)
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code: def __init__(
      self, name, features_dim=0, normalize=False, target_transform=""
  ):
      """Initialize parameters
  
      Args:
          name (str): Name of the dataset
          features_dim (uint): Dimension of the features. If not 0, PCA is performed
              on the features as the dimensionality reduction technique
          normalize (bool): Normalize the features
          target_transform (str): Transform the target values. Current options are
              'normalize' (Normalize the values), '' (Do nothing), 'binarize' (convert
              the values using a threshold defined per dataset)
  
      """
      self.name = None
      self.training_data = None
      self.test_data = None
      self.entities = None
  
      self.features_dim = features_dim
      self.feat_normalize = normalize
      self.target_transform = target_transform
  
imports: import logging
  import pandas
  import numpy
  from scipy.sparse import coo_matrix, isspmatrix_csr
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import normalize
  from recommenders.utils.python_utils import binarize
  from .geoimc_utils import length_normalize, reduce_dims
label_desc: Use image node_id 1 to create a new Dataset object  with example: obj = Dataset(name, features_dim, normalize, target_transform)