code: def test_empty_channel_detection(interpreter, layer):
      """Some models (mainly MobileNetV1) have kernels with many output channels full entirely of
      zeroes. The VWW model is one of these. This test confirms that the outputs of these channels,
      as computed by TensorFlow, are indeed not dependent upon the input values.
      """
  
      _, kernel, bias, output = _load_tflite_layer(interpreter, layer)
      kernel_data, _ = kernel
      bias_data, bias_quant = bias
      output_data, output_quant = output
      is_depthwise = _get_mobilenet_v1_layer_attributes(layer)[2]
      assert not is_depthwise
      assert kernel_data.shape[1] == kernel_data.shape[2] == 1
  
      out_channels = kernel_data.shape[3]
      fixed_channels = {}
  
      out_zero_point = output_quant["zero_points"][0]
      assert out_zero_point == -128
  
      for i in range(out_channels):
          # Skip over output channels with data
          if np.any(kernel_data[i, 0, 0, :]):
              continue
  
          scale = bias_quant["scales"][i] / output_quant["scales"][0]
          channel_constant = round(
              bias_data[i] * scale + out_zero_point
          )
          clipped = min(127, max(-128, channel_constant))
  
          out_channel_values = output_data[0, :, :, i].flatten()
          assert all(x == clipped for x in out_channel_values)
          fixed_channels[i] = clipped
  
      # Check if we are on the final convolution and skip the next test if so
      if layer + 1 >= MODEL_NUM_CONVS:
          return
  
      # We now need to compute values for the following depthwise layer
      depthwise_output = _load_tflite_layer(interpreter, layer + 1)[3][
          0
      ]
      is_depthwise = _get_mobilenet_v1_layer_attributes(layer + 1)[2]
      assert is_depthwise
  
      for i in fixed_channels:
          assert np.all(
              depthwise_output[:, :, :, i]
              == depthwise_output[0, 0, 0, i]
          )
  
imports: import numpy
  from PIL import Image
  import pytest
  import tvm
  import tvm.testing
  from tvm import meta_schedule, relay
  from tvm.testing.aot import AOTTestModel, run_and_check, AOTCompiledTestModel
  from tvm.relay.backend import Executor, Runtime
  from tvm.micro.testing.aot_test_utils import AOT_CORSTONE300_RUNNER
  from tvm.contrib.download import download_testdata
  from test_generalized_conv2d import change_ndarray_layout
label_desc: Use image node_id 3 for calling a global function with example usage: test_empty_channel_detection(interpreter, layer) without return types