code: def test_tflite_transpose_convolution(
      accel_type, ifm_shape, ofm_shape, kernel_shape, padding, has_bias
  ):
      np.random.seed(0)
      dilations = (1, 1)
      strides = (2, 2)
  
      @tf.function
      def conv2d_transpose(x):
          weight_shape = [
              kernel_shape[0],
              kernel_shape[1],
              ifm_shape[3],
              ofm_shape[3],
          ]
          weight = tf.constant(
              np.random.uniform(size=weight_shape), dtype=tf.float32
          )
          bias_shape = ofm_shape[3]
          bias = tf.constant(
              np.random.uniform(size=bias_shape), dtype=tf.float32
          )
          tf_strides = [1, strides[0], strides[1], 1]
          op = tf.nn.conv2d_transpose(
              x,
              weight,
              output_shape=ofm_shape,
              strides=tf_strides,
              padding=padding,
              dilations=dilations,
          )
          if has_bias:
              op = tf.nn.bias_add(op, bias)
          return op
  
      infra.compare_tvm_with_tflite(
          conv2d_transpose,
          [ifm_shape],
          accel_type=accel_type,
          enable_cascader=is_u55_accel_type(accel_type),
      )
  
imports: import pytest
  import numpy
  import tflite.Model
  import tvm
  import tensorflow
  from tvm import relay
  from tvm.relay.backend.contrib.ethosu import util
  from tvm.relay.op.contrib.ethosu import partition_for_ethosu
  from tvm.testing.aot import generate_ref_data
  from .None import infra
label_desc: Use image node_id 42 for calling a global function with example usage: test_tflite_transpose_convolution(accel_type, ifm_shape, ofm_shape, kernel_shape, padding, has_bias) and returns: op