def METHOD_NAME():
    if ndim == 0:
        inputs = []
        for i in range(num_inputs):
            inputs.append([np.float32(np.random.random()) for _ in range(batch_size)])
        return inputs
    inputs = [[] for _ in range(num_inputs)]
    for i in range(batch_size):
        base_shape = np.random.randint(1, max_extent, [ndim])
        for j in range(num_inputs):
            shape = list(base_shape)
            if variable_axis is not None:
                shape[variable_axis] = np.random.randint(1, 10)
            inputs[j].append(np.random.random(shape).astype(np.float32))
    return inputs