:]) return h.outer.Sum(in[:origLen]) } func (h *HMAC) Write(p []byte) (n int, err error) { return h.inner.Write(p) } func (h *HMAC) Size() int { return h.outer.Size() } func (h *HMAC) BlockSize() int { return h.inner.BlockSize() } func (h *HMAC) Reset() { if h.marshaled { if err := h.inner.(marshalable).UnmarshalBinary(h.ipad); err != nil { panic(err) } return } h.inner.Reset() h.inner.Write(h.ipad) // If the underlying hash is marshalable, we can save some time by saving a // copy of the hash state...

:]) return h.outer.Sum(in[:origLen]) } func (h *HMAC) Write(p []byte) (n int, err error) { return h.inner.Write(p) } func (h *HMAC) Size() int { return h.outer.Size() } func (h *HMAC) BlockSize() int { return h.inner.BlockSize() } func (h *HMAC) Reset() { if h.marshaled { if err := h.inner.(marshalable).UnmarshalBinary(h.ipad); err != nil { panic(err) } return } h.inner.Reset() h.inner.Write(h.ipad) // If the underlying hash is marshalable, we can save some time by saving a // copy of the hash state...

        `element_spec` argument of `tf.data.experimental.load` when loading the
        previously saved dataset.
    *   Add `.element_spec` property to `tf.data.DatasetSpec` to access the
        inner spec. This can be used to extract the structure of nested
        datasets.
    *   Add `tf.data.experimental.AutoShardingPolicy.HINT` which can be used to