parameters: (self, value, name=None)
docstring: Unstack the values of a `Tensor` in the TensorArray.
  
      If input value shapes have rank-`R`, then the output TensorArray will
      contain elements whose shapes are rank-`(R-1)`.
  
      Args:
        value: (N+1)-D.  Tensor of type `dtype`.  The Tensor to unstack.
        name: A name for the operation (optional).
  
      Returns:
        A new TensorArray object with flow that ensures the unstack occurs.
        Use this object all for subsequent operations.
  
      Raises:
        ValueError: if the shape inference fails.
docstring_summary: Unstack the values of a `Tensor` in the TensorArray.
docstring_tokens: Unstack the values of a Tensor in the TensorArray .
function: def unstack(self, value, name=None):
      """Unstack the values of a `Tensor` in the TensorArray.
  
      If input value shapes have rank-`R`, then the output TensorArray will
      contain elements whose shapes are rank-`(R-1)`.
  
      Args:
        value: (N+1)-D.  Tensor of type `dtype`.  The Tensor to unstack.
        name: A name for the operation (optional).
  
      Returns:
        A new TensorArray object with flow that ensures the unstack occurs.
        Use this object all for subsequent operations.
  
      Raises:
        ValueError: if the shape inference fails.
      """
      with ops.name_scope(name, "TensorArrayUnstack", [self._handle, value]):
        num_elements = array_ops.shape(value)[0]
        return self.scatter(
            indices=math_ops.range(0, num_elements), value=value, name=name)
function_tokens: def unstack ( self , value , name = None ) : with ops . name_scope ( name , "TensorArrayUnstack" , [ self . _handle , value ] ) : num_elements = array_ops . shape ( value ) [ 0 ] return self . scatter ( indices = math_ops . range ( 0 , num_elements ) , value = value , name = name )