code: def xavier_uniform(shape, random_state=None, dtype=np.float32):
      """Return a numpy array by performing 'Xavier' initializer
      also known as 'Glorot' initializer on Uniform distribution.
  
      Parameters
      ----------
      shape : int or tuple of ints
          Output shape.
      random_state : int or np.random.RandomState, optional
          If an integer is given, it will be used as seed value for creating a RandomState.
      dtype : str or dtype
          Returned data-type for the output array.
  
      Returns
      -------
      out : ndarray
          Output matrix.
  
      References
      ----------
      ** Xavier Glorot and Yoshua Bengio (2010):
      [Understanding the difficulty of training deep feedforward neural networks.
      International conference on artificial intelligence and statistics.]
      (http://www.jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)
      """
      assert len(shape) == 2  # only support matrix
      std = np.sqrt(2.0 / np.sum(shape))
      limit = np.sqrt(3.0) * std
      return uniform(shape, -limit, limit, random_state, dtype)
  
file_path: cornac/cornac/utils/init_utils.py
imports: import numpy
  from .common import get_rng
label_desc: Use image node_id 7 for calling a global function with example usage: xavier_uniform(shape, random_state, dtype) and returns: uniform