Shinsuke Sugaya <******@****.***> 1452930635 +0900

Shinsuke Sugaya <******@****.***> 1515316511 +0900

  let summary = "Draws samples from a multinomial distribution.";

  let arguments = (ins
    Arg<TF_IntOrFpTensor, [{2-D Tensor with shape `[batch_size, num_classes]`.  Each slice `[i, :]`
represents the unnormalized log probabilities for all classes.}]>:$logits,
    Arg<TF_Int32Tensor, [{0-D.  Number of independent samples to draw for each row slice.}]>:$num_samples,

            `TextVectorization`. Multi-hot encoding will no longer automatically
            uprank rank 1 inputs, so these layers can now multi-hot encode
            unbatched multi-dimensional samples.
        *   Added a new output mode `"one_hot"` for `CategoryEncoding`,
            `StringLookup`, `IntegerLookup`, which will encode each element in

by Dr._ Wallis, _I mention'd a Method by which I had found some general
Theorems about squaring Curvilinear Figures, or comparing them with the
Conic Sections, or other the simplest Figures with which they may be
compared. And some Years ago I lent out a Manuscript containing such
Theorems, and having since met with some Things copied out of it, I have