// CHECK: tfrt.while [[pred_0]]#1 @"while_body_add2/tfrt_body_10"
  // CHECK-SAME: parallel_iterations(10)
  // CHECK: [[pred_1:%.*]]:2 = tfrt.call @"while_cond_lt9/tfrt_predicate"
  // CHECK: tfrt.while [[pred_1]]#1 @"while_body_add2/tfrt_body_1"
  // CHECK-SAME: parallel_iterations(1)

		}

	case *Interface:
		// Two interface types are identical if they describe the same type sets.
		// With the existing implementation restriction, this simplifies to:
		//
		// Two interface types are identical if they have the same set of methods with
		// the same names and identical function types, and if any type restrictions
		// are the same. Lower-case method names from different packages are always

}

// CHECK-LABEL: @conv_3_fn
// CHECK: tf.XlaCallModule
// CHECK-SAME: _entry_function = @composite_conv_fn_6, _original_entry_function = "composite_conv_fn_6"
// CHECK: tf.XlaCallModule
// CHECK-SAME: _entry_function = @composite_conv_fn_5, _original_entry_function = "composite_conv_fn_5"

// CHECK-LABEL: @conv_1_fn
// CHECK: tf.XlaCallModule

  // CHECK-DAG: %[[VAL2:.+]] = "tfl.conv_2d"(%arg0, %[[VAL0]], %[[VAL1]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}>
  // CHECK: return %[[VAL2]]
  %0 = "tfl.pseudo_const"() {value = dense<42> : tensor<16x1x1x8xi8>} : () -> tensor<16x1x1x8xf32>

		}

	case *Interface:
		// Two interface types are identical if they describe the same type sets.
		// With the existing implementation restriction, this simplifies to:
		//
		// Two interface types are identical if they have the same set of methods with
		// the same names and identical function types, and if any type restrictions
		// are the same. Lower-case method names from different packages are always

  %3 = "tf.AddV2"(%2, %1): (tensor<15x28x28x1xf32>, tensor<1xf32>) -> tensor<15x28x28x1xf32>
  func.return %2 : tensor<15x28x28x1xf32>

}

// CHECK: %[[maxpool_i8:.*]] = "tf.MaxPool"
// CHECK-SAME: (tensor<*xi8>) -> tensor<*xi8>
// CHECK: %[[reshape_i8:.*]] = "tf.Reshape"(%[[maxpool_i8]]
// CHECK-SAME: (tensor<*xi8>, tensor<2xi32>) -> tensor<*xi8>
// CHECK: %[[scast:.*]] = "quantfork.scast"(%[[reshape_i8]]
// CHECK: %[[matmul:.*]] = "tf.PartitionedCall"(%[[scast]]
// CHECK-SAME: f = @composite_matmul_fn_1

            // Redirect stderr to stdout when both stdout and stderr are attached to a console. Assume that they are attached to the same console
            // This avoids interleaving problems when stdout and stderr end up at the same location
            Console console = consoleFor(stdout, consoleMetaData, renderer.getColourMap());

// MergeGateways combines multiple gateways targeting the same workload into a single logical Gateway.
// Note that today any Servers in the combined gateways listening on the same port must have the same protocol.
// If servers with different protocols attempt to listen on the same port, one of the protocols will be chosen at random.

// CHECK:             tfl.yield
// CHECK-SAME:  (tensor<i1>) -> ()
// CHECK:             [[VAL_30:%.*]]:7 =
// CHECK: call @tfl.while_body
// CHECK:             tfl.yield
// CHECK-SAME: (tensor<i32>, tensor<i32>, tensor<*xf32>, tensor<4x2xf32>, tensor<4x2xf32>, tensor<*xf32>, tensor<4x4x3xf32>) -> ()

// CHECK-LABEL:   func private @tfl.while_cond(