def METHOD_NAME(input_shape, input_dtype="float32",
                  batch_size=1, max_int_value=35,
                  newaxis=True, is_one_dim=False):
    np.random.seed(1024)
    if input_dtype in ["uint8", "int8", "int32", "int64"]:
        if is_one_dim:
            return np.random.randint(1, max_int_value, batch_size).astype(input_dtype)
        dummy_input = np.random.randint(1, max_int_value, input_shape).astype(input_dtype)
    else:
        if is_one_dim:
            return np.random.randn(batch_size).astype(input_dtype)
        dummy_input = np.random.randn(*input_shape).astype(input_dtype)
    # handle the case that the shape of the input is one-dimensional
    if newaxis is False:
        return np.repeat(dummy_input, batch_size, axis=0)
    return np.repeat(dummy_input[np.newaxis, :], batch_size, axis=0)