EXPECT_NE(float_tensor, nullptr);
      // If the tensor is a weight, it should have type INT8, otherwise it
      // should stay with type FLOAT32.
      // If the tensor is a bias, it should have type FLOAT32.
      //
      // Check with float_tensor name since quantized tensor
      // may be renamed.
      if (float_tensor->name()->str() == "conv_bias") {

  ASSERT_THAT(weights_zero_points, SizeIs(out_channel_size));
  ASSERT_THAT(input_tensor->quantization->scale, SizeIs(1));
  ASSERT_THAT(output_tensor->quantization->scale, SizeIs(1));

  const float eps = 1e-7;

  // Bias scale should be input * per_channel_weight_scale.
  for (size_t i = 0; i < out_channel_size; i++) {
    EXPECT_THAT(bias_scales[i], FloatNear(input_tensor->quantization->scale[0] *

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_BiasAddOp : TF_Op<"BiasAdd", [Pure, TF_LayoutSensitiveInterface]> {
  let summary = "Adds `bias` to `value`.";

  let description = [{
This is a special case of `tf.add` where `bias` is restricted to be 1-D.
Broadcasting is supported, so `value` may have any number of dimensions.
  }];

  let arguments = (ins

        *   The new backend is used for 8 bits full integer post-training
            quantization
        *   The new backend removes the redundant rescales and fixes some bugs
            (shared weight/bias, extremely small scales, etc)
        *   Set `experimental_new_quantizer` in tf.lite.TFLiteConverter to False
            to disable this change
*   `tf.keras`
    *   `tf.keras.metrics.AUC` now support logit predictions.

// This differs from a normal TF::AddOp with respect to how the data_format
// is handled, which can optionally require a general broadcast of the
// 'bias' term in a way that is not compatible with the standard left-padded
// broadcast semantics (i.e. NCHW will broadcast into dimension 1).
// The correct 'bias' broadcast will be synthesized manually.
class ConvertBiasAddOp : public OpRewritePattern<TF::BiasAddOp> {
 public:

* Remove gender bias in docs for handling errors. PR [#780](https://github.com/tiangolo/fastapi/pull/780). Original idea in PR [#761](https://github.com/tiangolo/fastapi/pull/761) by [@classywhetten](https://github.com/classywhetten).