// panicking goroutine. Executing a call to recover inside a deferred
// function (but not any function called by it) stops the panicking sequence
// by restoring normal execution and retrieves the error value passed to the
// call of panic. If recover is called outside the deferred function it will
// not stop a panicking sequence. In this case, or when the goroutine is not

condition is reported, including the value of the argument to <code>panic</code>.
This termination sequence is called <i>panicking</i>.
</p>

<pre>
panic(42)
panic("unreachable")
panic(Error("cannot parse"))
</pre>

<p>
The <code>recover</code> function allows a program to manage behavior
of a panicking goroutine.
Suppose a function <code>G</code> defers a function <code>D</code> that calls

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either a fixed-length computed hash, or parsed from a // field which has the same length limitation. // // Another reasonable approach might be to return a randomized slice if // we encounter an error, which would break the connection, but avoid // panicking. This would perhaps be safer but significantly more // confusing to users. panic("tls13: label or context too long") } hkdfLabel := make([]byte, 0, 2+1+len("tls13 ")+len(label)+1+len(context)) hkdfLabel = byteorder.BEAppendUint16(hkdfLabel, uint16(length))...

        [example](https://github.com/tensorflow/models/blob/master/official/modeling/grad_utils.py)
        usage.
    *   Experimental support of
        [all reduce gradient packing](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/CollectiveHints)
        to allow overlapping gradient aggregation with backward path
        computation.