def METHOD_NAME(inputs,
                       filters,
                       kernel_size,
                       strides=(1, 1),
                       padding="valid",
                       data_format="NHWC",
                       filter_format="HWIO"):
  dtype = inputs.dtype
  c_axis = -1 if data_format == "NHWC" else 1
  nchan = inputs.shape[c_axis]
  if filter_format == "HWIO":
    weights_shape = (kernel_size[0], kernel_size[1], nchan, filters)
  else:
    assert filter_format == "OIHW"
    weights_shape = (filters, nchan, kernel_size[0], kernel_size[1])
  weights = constant_op.constant(np.random.randn(*weights_shape), dtype=dtype)
  biases_shape = (filters,)
  biases = constant_op.constant(np.random.randn(*biases_shape), dtype=dtype)
  padding = padding.upper()
  if data_format == "NHWC":
    strides = [1] + list(strides) + [1]
  else:
    assert data_format == "NCHW"
    strides = [1, 1] + list(strides)
  return fused_conv2d_bias_activation(
      inputs,
      weights,
      biases,
      strides=strides,
      padding=padding,
      data_format=data_format,
      filter_format=filter_format,
      activation_mode="Relu")