class_docstr: A variable backed by a batched numpy "stack" with a cache for the top.
  
  The purpose of the cache is to make reads from and writes to the top
  of the stack cheaper than they would be otherwise.
  
  The namedtuple structure exposes the full state of the variable, and is useful
  for testing, passing through flatten/unflatten operations, and general
  symmetry with the TensorFlow backend.
class_docstr_tok: ["A","variable","backed","by","a","batched","numpy","``","stack","''","with","a","cache","for","the","top",".","The","purpose","of","the","cache","is","to","make","reads","from","and","writes","to","the","top","of","the","stack","cheaper","than","they","would","be","otherwise",".","The","namedtuple","structure","exposes","the","full","state","of","the","variable",",","and","is","useful","for","testing",",","passing","through","flatten\/unflatten","operations",",","and","general","symmetry","with","the","TensorFlow","backend","."]
docstr: Returns the batch of top values.
  
  Args:
    name: Optional name for the op.
  
  Return:
    val: Read of the current variable value.
docstr_tok: ["Returns","the","batch","of","top","values",".","Args",":","name",":","Optional","name","for","the","op",".","Return",":","val",":","Read","of","the","current","variable","value","."]
code: def read(self, name=None):
      """Returns the batch of top values.
  
      Args:
        name: Optional name for the op.
  
      Return:
        val: Read of the current variable value.
      """
      del name
      return self.current
  
imports: import collections
  import numpy
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 1 for calling the FullNumpyVariable obj's underlying member method code with example usage: obj.read(name) and returns: self