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();
  }

            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.

    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
		}
	}

	l.UpdatedAt = t // update to the latest time index

	if since >= 24 {
		l.Bins = [24]tierStats{}
		return
	}

	for lastIdx != idx {
		lastIdx = (lastIdx + 1) % 24
		l.Bins[lastIdx] = tierStats{}
	}
}

func (l *lastDayTierStats) clone() lastDayTierStats {
	clone := lastDayTierStats{
		UpdatedAt: l.UpdatedAt,
	}
	copy(clone.Bins[:], l.Bins[:])
	return clone
}

  public static HashFunction goodFastHash(int minimumBits) {
    int bits = checkPositiveAndMakeMultipleOf32(minimumBits);

    if (bits == 32) {
      return Murmur3_32HashFunction.GOOD_FAST_HASH_32;
    }
    if (bits <= 128) {
      return Murmur3_128HashFunction.GOOD_FAST_HASH_128;
    }

    // Otherwise, join together some 128-bit murmur3s
    int hashFunctionsNeeded = (bits + 127) / 128;