class_docstr: Handles additions resulting in a Diag operator.
class_docstr_tok: ["Handles","additions","resulting","in","a","Diag","operator","."]
code: def can_add(self, op1, op2):
      types = {_type(op1), _type(op2)}
      return not types.difference(_DIAG_LIKE)
  
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 1 for calling the _AddAndReturnDiag obj's underlying member method code with example usage: obj.can_add(op1, op2) and returns: unknown