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.");
    }

		pwd                = password("pass")
		jsVal              = []byte(`{"Name":"test","Val":"test"}`)
		js                 = JSON(jsVal)
		esVal              = []byte(`{"Name":"test","Val":"test"}`)
		es                 = ExampleStruct{Name: "test", Val: "test"}
		intVal   intType   = 1
		floatVal floatType = 1.23
	)

	results := []struct {
		SQL           string
		NumericRegexp *regexp.Regexp

}

Status MlirAbstractOp::Execute(absl::Span<AbstractTensorHandle*> retvals,
                               int* num_retvals) {
  OperationState* state;
  TF_RETURN_IF_ERROR(Create(operands_, &state));
  Operation* op = function_context_->CreateOperationFromState(*state);
  *num_retvals = op->getNumResults();
  for (int i = 0; i < *num_retvals; i++)
    retvals[i] = new MlirTensor(op->getResult(i));
  return absl::OkStatus();
}

            url_ = url;
        }

        /**
         * @return Returns the cal.
         */
        public Calendar getCal() {
            return cal;
        }

        /**
         * @param cal
         *            The cal to set.
         */
        public void setCal(final Calendar cal) {
            this.cal = cal;
        }
    }

    /**
     *
     */

    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()));
    }

// CHECK:  func.func @_ifrt_program__func(%arg0: tensor<1x3xf32>)
// CHECK-SAME: __tpu_compile_metadata_text = "args { dtype: DT_FLOAT shape { dim { size: 1 } dim { size: 3 } } kind: PARAMETER sharding { } is_bounded_dynamic_dim: false } retvals { sharding { } } num_replicas: 1 num_cores_per_replica: 1 "
// CHECK-SAME: tfrt_ifrt_serving.program_id = [[PROGRAM_ID]] : i64
// CHECK:      return

			result.combine(e.Trim.TrimChars.analyze(s))
		}
		if e.Trim.TrimFrom != nil {
			result.combine(e.Trim.TrimFrom.analyze(s))
		}
		return result

	case sqlFnSubstring:
		errVal := fmt.Errorf("Invalid argument(s) to %s", string(funcName))
		result.combine(e.Substring.Expr.analyze(s))
		switch {
		case e.Substring.From != nil:
			result.combine(e.Substring.From.analyze(s))

	AssertEqual(t, err, nil)

	defVal, ok := columnType.DefaultValue()
	AssertEqual(t, defVal, "null")
	AssertEqual(t, ok, true)

	columnType2, err := findColumnType(tableName, "active")
	AssertEqual(t, err, nil)

	defVal, ok = columnType2.DefaultValue()
	bv, _ := strconv.ParseBool(defVal)
	AssertEqual(t, bv, false)
	AssertEqual(t, ok, true)

    return op.emitOpError(
        llvm::formatv(kBadArrayAttrLengthMsg, tensorflow::kOutputShardingAttr,
                      op.getNumResults(), output_shardings.size()));

  // Set retvals metadata in proto.
  for (auto output_sharding_and_idx : llvm::enumerate(output_shardings))
    if (failed(SetOpSharding(op, output_sharding_and_idx.value(),
                             tensorflow::kOutputShardingAttr,