class_docstr: Initializes parameters with values specified by a provided numpy array.
  
  Same functionality as Constant except serialization needs to dump
  tensor values into np array
  
  Args:
      vals (ndarray or tensor, optional): Values to assign to the tensor elements
class_docstr_tok: ["Initializes","parameters","with","values","specified","by","a","provided","numpy","array",".","Same","functionality","as","Constant","except","serialization","needs","to","dump","tensor","values","into","np","array","Args",":","vals","(","ndarray","or","tensor",",","optional",")",":","Values","to","assign","to","the","tensor","elements"]
docstr: Returns description of the object as a dict. Transfers the
  tensors back to a numpy array.
docstr_tok: ["Returns","description","of","the","object","as","a","dict",".","Transfers","the","tensors","back","to","a","numpy","array","."]
code: def get_description(self):
      """
      Returns description of the object as a dict. Transfers the
      tensors back to a numpy array.
      """
      desc = super(Array, self).get_description()
      if isinstance(desc["config"]["val"], Tensor):
          desc["config"]["val"] = desc["config"]["val"].get()
      return desc
  
imports: import numpy
  from neon import NervanaObject
  from neon.backends.backend import Tensor
label_desc: Use image node_id 1 for calling the Array obj's underlying member method code with example usage: obj.get_description() and returns: desc