class_docstr: Handles additions resulting in a Diag operator.
class_docstr_tok: ["Handles","additions","resulting","in","a","Diag","operator","."]
code: def _add(self, op1, op2, operator_name, hints):
      return linear_operator_diag.LinearOperatorDiag(
          diag=op1.diag_part() + op2.diag_part(),
          is_non_singular=hints.is_non_singular,
          is_self_adjoint=hints.is_self_adjoint,
          is_positive_definite=hints.is_positive_definite,
          name=operator_name,
      )
  
imports: import abc
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy.gen import tensor_shape
  from tensorflow_probability.python.internal.backend.numpy import numpy_array
  from tensorflow_probability.python.internal.backend.numpy import debugging
  from tensorflow_probability.python.internal.backend.numpy.gen import linear_operator
  from tensorflow_probability.python.internal.backend.numpy.gen import linear_operator_diag
  from tensorflow_probability.python.internal.backend.numpy.gen import linear_operator_full_matrix
  from tensorflow_probability.python.internal.backend.numpy.gen import linear_operator_identity
  from tensorflow_probability.python.internal.backend.numpy.gen import linear_operator_lower_triangular
  import numpy
  from tensorflow_probability.python.internal.backend.numpy import linalg_impl
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy.gen import tensor_shape
  from tensorflow_probability.python.internal.backend.numpy import private
label_desc: Use image node_id 2 for calling the _AddAndReturnDiag obj's underlying member method code with example usage: obj._add(op1, op2, operator_name, hints) and returns: linear_operator_diag