我试图提取使用tf.keras.preprocessing.timeseries_dataset_from_array窗口化的1D信号的某些特征，从中我获得了一个tf.data.Dataset对象ds（参见下面的代码）。理想情况下，我希望使用数据集的内置map方法将特征函数（使用numpy和scipy）Map到数据集上。
然而，当我天真地尝试这样做时：

import numpy as np
import scipy as sc
import tensorflow as tf

def feat1_func(x, sf, axis):
    x = np.asarray(x)
    feat1_value = np.apply_along_axis(
        lambda vals: sc.integrate.trapezoid(abs(vals), dx=1 / sf), axis=axis, arr=x
    )
    return feat1_value

features = ['feat1']
feature_map = {'feat1': feat1_func}

x = np.random.rand(100, 5)
y = np.random.randint(low=0, high=2, size=100)

sequence_length = 10
sequence_stride = 3

ds = tf.keras.preprocessing.timeseries_dataset_from_array(
        data=x,
        targets=y,
        sequence_length=sequence_length,
        sequence_stride=sequence_stride,
        batch_size=None,
        shuffle=False,
    )

feat_lambda = lambda x, y: (np.array([feature_map[ft](x, sf=1000, axis=0) for ft in features]), y)
ds = ds.map(feat_lambda)

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我收到以下错误消息：

NotImplementedError: Cannot convert a symbolic tf.Tensor (args_0:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported.

型
这个问题最简单的解决方法是什么？当Map发生时，是否可以将符号Tensor转换为渴望Tensor？