var retval []certificates.CertificateSigningRequestCondition
	for i, c := range csr.Status.Conditions {
		if c.Type == conditionType {
			retval = append(retval, csr.Status.Conditions[i])
		}
	}
	return retval
}

// getValidationOptions returns the validation options to be

    if (mul) {
      auto c = ops::Mul(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    } else {
      auto c = ops::Add(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    }

                           ForwardOperation*);

// Make the call to `Tape::RecordOperation`.
Status Execute(AbstractOperation*, AbstractContext*,
               absl::Span<AbstractTensorHandle*> retvals, int* num_retvals,
               ForwardOperation*, Tape*, const GradientRegistry&);

}  // namespace internal
}  // namespace gradients
}  // namespace tensorflow

				clause.Type == accepts.MediaTypeType && clause.SubType == "*",
				clause.Type == "*" && clause.SubType == "*":
				if retVal, ret := acceptMediaTypeOptions(clause.Params, accepts, endpoint); ret {
					return retVal, true
				}
			}
		}
	}

	return MediaTypeOptions{}, false

 Sum 2
 Switch 247
 TensorArrayGatherV3 8
 TensorArrayGradV3 17
 TensorArrayReadV3 9
 TensorArrayScatterV3 8
 TensorArraySizeV3 4
 TensorArrayV3 8
 TensorArrayWriteV3 9
 Unique 2
 VariableV2 164
 _Retval 5
cluster 0 size 440
 Abs 40
 AddN 1
 Any 41
 Cast 40
 ConcatV2 2
 Const 95
 ExpandDims 2
 IsInf 1
 IsNan 40
 L2Loss 40
 LogicalOr 1
 Max 41
 Minimum 1
 Mul 82
 Pack 3

	MOVW	(g_stack+stack_hi)(R3), R13

	// make room for args, retval and g
	SUB	$24, R13

	// save g
	MOVW	g, R3
	MOVW	R3, 20(R13)

	// g = m->gsignal
	MOVW	m_gsignal(R0), g

	// load args and call sighandler
	ADD	$4,R13,R5
	MOVM.IA	[R1-R3], (R5)
	BL	runtime·sighandler(SB)
	MOVW	16(R13), R0			// retval

	// restore g
	MOVW	20(R13), g

	// call noted(R0)

          bytes.length);
      return padToLong();
    }

    @Override
    public long padToLong() {
      long retVal = (bytes[0] & 0xFF);
      for (int i = 1; i < Math.min(bytes.length, 8); i++) {
        retVal |= (bytes[i] & 0xFFL) << (i * 8);
      }
      return retVal;
    }

    @Override
    void writeBytesToImpl(byte[] dest, int offset, int maxLength) {

                             int* num_retvals) override {
    std::vector<TFE_TensorHandle*> outputs(*num_retvals);
    TF_Status status;
    device_.execute(tensorflow::wrap(op), num_retvals, outputs.data(), &status,
                    info_);
    if (status.status.ok()) {
      for (int i = 0; i < *num_retvals; ++i) {
        retvals[i] = tensorflow::unwrap(outputs[i]);

            tile_assignment_dimensions: 1
            tile_assignment_devices: 0
            tile_assignment_devices: 1
          }
          is_bounded_dynamic_dim: false
        }
        retvals { sharding {} }
        num_replicas: 1
        num_cores_per_replica: 2
        use_spmd_for_xla_partitioning: true
        compile_options {}
      )pb",
      &expected_compile_metadata));

      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }
  Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                 int* num_retvals) override {
    auto* tf_opdesc = op_.release();
    if (tf_opdesc == nullptr) {
      return errors::InvalidArgument("AbstractOp is incomplete.");
    }