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);
  }

              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

    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();
  }

    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.

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>

 *
 * Note that [java.util.zip.Deflater] is hardcoded to use 15 bits (32 KiB) for
 * `client_max_window_bits` and [java.util.zip.Inflater] is hardcoded to use 15 bits (32 KiB) for
 * `server_max_window_bits`. This harms our ability to support these parameters:
 *
 *  * If `client_max_window_bits` is less than 15, OkHttp must close the web socket with code 1010.

pkg math/bits (windows-amd64), const UintSize = 64
pkg math/bits, const UintSize ideal-int
pkg math/bits, func LeadingZeros(uint) int
pkg math/bits, func LeadingZeros16(uint16) int
pkg math/bits, func LeadingZeros32(uint32) int
pkg math/bits, func LeadingZeros64(uint64) int
pkg math/bits, func LeadingZeros8(uint8) int
pkg math/bits, func Len(uint) int
pkg math/bits, func Len16(uint16) int
pkg math/bits, func Len32(uint32) int

    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(

	// map header, size 2
	// write "Bins"
	err = en.Append(0x82, 0xa4, 0x42, 0x69, 0x6e, 0x73)
	if err != nil {
		return
	}
	err = en.WriteArrayHeader(uint32(24))
	if err != nil {
		err = msgp.WrapError(err, "Bins")
		return
	}
	for za0001 := range z.Bins {
		err = z.Bins[za0001].EncodeMsg(en)
		if err != nil {
			err = msgp.WrapError(err, "Bins", za0001)
			return
		}
	}