`tape` is required when a `Tensor` loss is passed.

This error message usually occurs when you are using TensorFlow's automatic differentiation to compute gradients for backpropagation, and you have passed a tensor as the loss argument to the `tf.GradientTape()` context manager. The `tf.GradientTape()` method requires a tensor to be watched in order to compute gradients, and the tensor you pass as the loss argument is not automatically watched by default.

To fix this error, you can explicitly tell the tape to watch the tensor by calling the `tape.watch()` method on the tensor before using it as the loss argument.

For example:

import tensorflow as tf

x = tf.constant(3.0)
y = tf.constant(4.0)

with tf.GradientTape() as tape:
    tape.watch(x)
    loss = x * y

grad = tape.gradient(loss, x)
print(grad.numpy())  # Output: 4.0

Here, we explicitly tell the tape to watch the `x` tensor by calling `tape.watch(x)`, so that it can compute the gradient of the loss with respect to `x`.