when {
                k <= bias -> TMIN
                k >= bias + TMAX -> TMAX
                else -> k - bias
              }
            if (q < t) break
            result.writeByte((t + ((q - t) % (BASE - t))).punycodeDigit)
            q = (q - t) / (BASE - t)
          }

          result.writeByte(q.punycodeDigit)
          bias = adapt(delta, h + 1, h == b)
          delta = 0

    A.reset(A_raw);
  }
  // Bias
  float Bias_vals[] = {2.0f, 3.0f};
  int64_t Bias_dims[] = {2};
  AbstractTensorHandlePtr Bias;
  {
    AbstractTensorHandle* Bias_raw;
    status_ = TestTensorHandleWithDims<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), Bias_vals, Bias_dims, 1, &Bias_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    Bias.reset(Bias_raw);
  }

    TF_RETURN_IF_ERROR(forward_attrs_.Get("data_format", &data_format));

    // Grad for A
    grad_inputs[0] = upstream_grad;
    grad_inputs[0]->Ref();

    // Grad for bias
    std::string name = "bias_add_grad";
    TF_RETURN_IF_ERROR(BiasAddGrad(ctx, upstream_grad, &grad_inputs[1],
                                   data_format.c_str(), name.c_str()));

    return absl::OkStatus();
  }

    }

    /*
     * We need the top SIGNIFICAND_BITS + 1 bits, including the "implicit" one bit. To make rounding
     * easier, we pick out the top SIGNIFICAND_BITS + 2 bits, so we have one to help us round up or
     * down. twiceSignifFloor will contain the top SIGNIFICAND_BITS + 2 bits, and signifFloor the
     * top SIGNIFICAND_BITS + 1.
     *

    Random random = new Random();
    int nKeys = keys.size();
    long blah = 0;
    for (int i = 0; i < reps; i++) {
      Integer key = keys.get(random.nextInt(nKeys));
      // This range is [-5, 4] - slight negative bias so we often hit zero, which brings the
      // auto-removal of zeroes into play.
      int delta = random.nextInt(10) - 5;
      blah += delta;
      if (delta >= 0) {
        multiset.add(key, delta);

    Random random = new Random();
    int nKeys = keys.size();
    long blah = 0;
    for (int i = 0; i < reps; i++) {
      Integer key = keys.get(random.nextInt(nKeys));
      // This range is [-5, 4] - slight negative bias so we often hit zero, which brings the
      // auto-removal of zeroes into play.
      int delta = random.nextInt(10) - 5;
      blah += delta;
      if (delta >= 0) {
        multiset.add(key, delta);

            supports equivalent functionality. `max_tokens` argument renamed to
            `num_tokens`.
        *   `Discretization` added `num_bins` argument for learning bins
            boundaries through calling `adapt` on a dataset. Renamed `bins`
            argument to `bin_boundaries` for specifying bins without `adapt`.
    *   Improvements to model saving/loading:
        *   `model.load_weights` now accepts paths to saved models.

sebegini
begitu
begitukah
begitulah
begitupun
sebegitu
belum
belumlah
sebelum
sebelumnya
sebenarnya
berapa
berapakah
berapalah
berapapun
betulkah
sebetulnya
biasa
biasanya
bila
bilakah
bisa
bisakah
sebisanya
boleh
bolehkah
bolehlah
buat
bukan
bukankah
bukanlah
bukannya
cuma
percuma
dahulu
dalam
dan
dapat
dari
daripada
dekat
demi

implementation must:
</p>

<ul>
	<li>Represent integer constants with at least 256 bits.</li>

	<li>Represent floating-point constants, including the parts of
	    a complex constant, with a mantissa of at least 256 bits
	    and a signed binary exponent of at least 16 bits.</li>

	<li>Give an error if unable to represent an integer constant
	    precisely.</li>

    assertThat(parse("permessage-deflate; client_max_window_bits=8")).isEqualTo(
      WebSocketExtensions(perMessageDeflate = true, clientMaxWindowBits = 8),
    )
    assertThat(parse("permessage-deflate; client_max_window_bits=\"8\"")).isEqualTo(
      WebSocketExtensions(perMessageDeflate = true, clientMaxWindowBits = 8),
    )
    assertThat(parse("permessage-deflate; client_max_window_bits=15")).isEqualTo(