padding_value                 ; otherwise.
```
where `y = max(-k[1], 0)`, `x = max(k[1], 0)`.

Otherwise, the output tensor has rank `r` with dimensions
`[I, J, ..., L, num_diags, max_diag_len]` with values:

```
diagonal[i, j, ..., l, m, n]
  = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N,
    padding_value                 ; otherwise.
```

    // values will be undefined. So we mask them later. Set up the boolean
    // expression that tells us which entries, in the output shape, are out of
    // bounds and thus become the padding_value.
    Value x_in_bounds = rewriter.create<AndOp>(
        loc,
        rewriter.create<TF::GreaterEqualOp>(loc, b_false.getType(), n_plus_x,
                                            b_zero),