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

The `tape` refers to the GradientTape object in TensorFlow. It is used to record operations for automatic differentiation. When training a neural network, we want to compute the gradient of the loss function with respect to the model parameters, and the `tape` helps us do that by recording all the operations that happen during the forward pass.

In TensorFlow, we use the `tf.GradientTape()` context manager to record these operations. When we pass a `Tensor` loss to an optimizer, we need to wrap the optimizer's `minimize()` method inside a `tf.GradientTape()` block, so that the tape can record the operations necessary for gradient computation.

Here's an example:

import tensorflow as tf

# define your model and loss function
model = ...
loss_fn = ...

# define your optimizer
optimizer = tf.keras.optimizers.Adam()

# training loop
for batch in dataset:
    with tf.GradientTape() as tape:
        # forward pass
        predictions = model(batch['input'])
        loss = loss_fn(batch['target'], predictions)
    
    # calculate gradients
    gradients = tape.gradient(loss, model.trainable_variables)
    
    # update model parameters
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))