code: def test_tflite_conv2d_with_separate_channel_padding_legalize():
      dtype = "int8"
      ifm_shape = (1, 55, 34, 3)
      kernel_shape = (3, 2)
      strides = (1, 1)
      dilation = (2, 1)
      padding_ch = (1, 1)
  
      class ArePadOnGraph(ExprVisitor):
          """
          Visits the Graph recursively and checks if it contains 'nn.pad' op
          """
  
          def __init__(self):
              ExprVisitor.__init__(self)
              self.on_graph = False
  
          def visit_call(self, call):
              if isinstance(call.op, tvm.ir.Op):
                  if str(call.op.name) == "nn.pad":
                      self.on_graph = True
  
              return super().visit_call(call)
  
          def are_pad_on_graph(self, subgraph) -> bool:
              """
              This function recursively visits the graph and checks if 'nn.pad' op is on graph
              """
              self.visit(subgraph)
              return self.on_graph
  
      def create_tflite_graph():
          class Model(tf.Module):
              @tf.function
              def tf_function(self, x):
                  tf_strides = [1, strides[0], strides[1], 1]
                  op = tf.pad(
                      x,
                      [
                          [0, 0],
                          [0, 0],
                          [0, 0],
                          [padding_ch[0], padding_ch[1]],
                      ],
                      "CONSTANT",
                  )
                  # HWIO
                  weight_shape = [
                      kernel_shape[0],
                      kernel_shape[1],
                      ifm_shape[3] + padding_ch[0] + padding_ch[1],
                      3,
                  ]
                  weight = tf.constant(
                      np.random.uniform(size=weight_shape),
                      dtype=tf.float32,
                  )
                  return tf.nn.conv2d(
                      op,
                      weight,
                      strides=tf_strides,
                      padding="VALID",
                      dilations=dilation,
                  )
  
          model = Model()
          concrete_func = model.tf_function.get_concrete_function(
              tf.TensorSpec(ifm_shape, dtype=tf.float32)
          )
  
          # Convert the model
          def representative_dataset():
              for _ in range(100):
                  data = np.random.rand(*tuple(ifm_shape))
                  yield [data.astype(np.float32)]
  
          converter = tf.lite.TFLiteConverter.from_concrete_functions(
              [concrete_func]
          )
          converter.optimizations = [tf.lite.Optimize.DEFAULT]
          converter.representative_dataset = representative_dataset
          converter.target_spec.supported_ops = [
              tf.lite.OpsSet.TFLITE_BUILTINS_INT8
          ]
          converter.inference_input_type = tf.int8
          converter.inference_output_type = tf.int8
          tflite_model = converter.convert()
          return tflite_model
  
      def verify(ext_func):
          assert ArePadOnGraph().are_pad_on_graph(ext_func.body) == True
  
      conv2d_pattern_table = [
          (
              ethosu.ChannelPadParams.composite_name,
              ethosu.pad_pattern(),
              lambda pat: ethosu.ChannelPadParams(pat).is_valid(),
          ),
          (
              ethosu.QnnConv2DParams.composite_name,
              ethosu.qnn_conv2d_pattern(),
              lambda pat: ethosu.QnnConv2DParams(pat).is_valid(),
          ),
      ]
  
      tflite_graph = create_tflite_graph()
      tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
  
      mod, conv_params = relay.frontend.from_tflite(
          tflite_model,
          shape_dict={"input": ifm_shape},
          dtype_dict={"input": dtype},
      )
  
      mod["main"] = bind_params_by_name(mod["main"], conv_params)
      mod = partition_ethosu_by_table(mod, conv2d_pattern_table)
  
      mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
          legalize.Conv2DRewriter(),
          mod["tvmgen_default_ethos_u_main_0"],
      )
  
      verify(mod["tvmgen_default_ethos_u_main_0"])
  
imports: import pytest
  import math
  import numpy
  import tensorflow
  import tflite.Model
  import tvm
  from tvm import relay
  from tvm.relay.backend.contrib.ethosu import legalize, preprocess
  from tvm.relay import dataflow_pattern
  from tvm.relay.op.contrib import ethosu
  from tvm.relay.backend.contrib.ethosu import util, codegen
  from tvm.relay.build_module import bind_params_by_name
  from tvm.relay.frontend.tflite import get_pad_value
  from tvm.relay.expr_functor import ExprVisitor
  from .None import infra
label_desc: Use image node_id 7 for calling a global function with example usage: test_tflite_conv2d_with_separate_channel_padding_legalize() and returns: tflite_model, super, self, tf