def METHOD_NAME(self):
    self._data_type = np.float32
    # Very large tensor will always fail for gRPC because the Protobuf has a
    # hard limit on 2GBs for the size of input tensors. All backends except
    # plan backend should be able to handle payloads larger than 2GBs using
    # HTTP.
    very_large_tensor_shape = (
        math.trunc(3 * (1024 * 1024 * 1024) / np.dtype(self._data_type).itemsize),
    )
    self._very_large_in0 = np.random.random(very_large_tensor_shape).astype(
        self._data_type
    )
    # 1.9 GBs allows us to test gRPC with moderate sizes too.
    large_tensor_shape = (
        math.trunc(
            1.9 * (1024 * 1024 * 1024) // np.dtype(self._data_type).itemsize
        ),
    )
    self._large_in0 = np.random.random(large_tensor_shape).astype(self._data_type)
    small_tensor_shape = (1,)
    self._small_in0 = np.random.random(small_tensor_shape).astype(self._data_type)
    self._clients = (
        (httpclient, httpclient.InferenceServerClient("localhost:8000")),
        (grpcclient, grpcclient.InferenceServerClient("localhost:8001")),
    )