tf_device.return %2 : tensor<i64>
      }
      tf_executor.yield %1#0, %1#1 : tensor<i64>, tensor<i64>
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK:      tf_executor.island
// CHECK:      [[CONST_0:%.+]] = "tf.Const"
// CHECK-SAME: value = dense<1> : tensor<i64>
// CHECK:      tf_executor.yield [[CONST_0]]

// CHECK:           %[[REDUCE:.*]] = "stablehlo.reduce_window"(%[[INPUT]], %[[PAD_VAL]])
// CHECK:               <{window_dimensions = array<i64: 1, 3, 3, 1>,
// CHECK:                 window_strides = array<i64: 1, 2, 2, 1>}> ({
// CHECK:           ^bb0(%[[ARG0:.*]]: tensor<f32>, %[[ARG1:.*]]: tensor<f32>):
// CHECK:             %[[MAX:.*]] = stablehlo.maximum %[[ARG0]], %[[ARG1]] : tensor<f32>

      %outputs_3, %control_4 = tf_executor.island wraps "tf.Placeholder"() {device = "", dtype = "tfdtype$DT_FLOAT",...

      %axis = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64>
      %1 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3x!tf_type.qint8>, tensor<3x3x!tf_type.qint8>, tensor<i64>) -> tensor<6x3x!tf_type.qint8>
      func.return %1 : tensor<6x3x!tf_type.qint8>
    }
  })";
  CreateModule(kMlirModuleStr);

"builtin.module"() ({
  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):
    %1617 = "tf.While"(%arg0) {_lower_using_switch_merge = true, _num_original_outputs = 7 : i64, _read_only_resource_inputs = [], body = @main_while_body_4225150, cond = @main_while_cond_4225140, device = "", is_stateless = false, parallel_iterations = 10 : i64, shape_invariant} : (tensor<i32>) -> (tensor<i32>)
    "func.return"() : () -> ()

    %0 = "tf.Const"() {value = dense<1> : tensor<i64>} : () -> tensor<i64>
    %1 = "tf.Const"() {value = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>

    %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }
}

// CHECK-LABEL: quantize_conv_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x3x4x3xf32>

// CHECK:           %[[VAL_1:.*]] = "tf.Const"() <{value = dense<1> : tensor<i64>}> : () -> tensor<i64>
// CHECK:           return %[[VAL_1]] : tensor<i64>
// CHECK:         }
func.func @size_rank_one_i64(%arg0: tensor<f32>) -> tensor<i64> {
  %0 = mhlo.constant dense<1> : tensor<i64>
  func.return %0 : tensor<i64>
}

// CHECK-LABEL:   func @complex(

	resolveRnglistx := func(rnglistsBase, off uint64) uint64 {
		is64, _ := b.format.dwarf64()
		if is64 {
			off *= 8
		} else {
			off *= 4
		}
		off += rnglistsBase
		if uint64(int(off)) != off {
			b.error("DW_FORM_rnglistx offset out of range")
		}

		b1 := makeBuf(b.dwarf, b.format, "rnglists", 0, b.dwarf.rngLists)
		b1.skip(int(off))
		if is64 {
			off = b1.uint64()
		} else {

	_ = complex(f32, f32)
	_ = complex(f32, 1)
	_ = complex(f32, 1.0)
	_ = complex(f32, 'a')
	_ = complex(f64, f64)
	_ = complex(f64, 1)
	_ = complex(f64, 1.0)
	_ = complex(f64, 'a')
	_ = complex(f32 /* ERROR "mismatched types" */ , f64)
	_ = complex(f64 /* ERROR "mismatched types" */ , f32)
	_ = complex(1, 1)
	_ = complex(1, 1.1)
	_ = complex(1, 'a')
	complex /* ERROR "not used" */ (1, 2)