"gradients/MatMul", false, true);
    TF_Operation* matmul2 = MatMul(expected_graph_, s_, const0, const3,
                                   "gradients/MatMul_1", true, false);
    expected_grad_outputs[0] = {matmul1, 0};
    expected_grad_outputs[1] = {matmul2, 0};
  }

  TF_Tensor* FloatTensor2x2(const float* values) {

  TFE_ContextOptions* tfe_context_options_;
  TFE_Context* tfe_context_;
};

TEST_F(ShapeInferenceTest, InfersShapesFromInputShapes) {
  TFE_Op* matmul_op;
  matmul_op = TFE_NewOp(tfe_context_, "MatMul", status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);

  // Infer shape when everything is known.
  CheckOutputShapes(matmul_op,

* `tf.config.experimental.enable_tensor_float_32_execution`

 Ze=null;const Je=R({},["audio","video","img","source","image","track"]);let Qe=null;const et=R({},["alt","class","for","id","label","name","pattern","placeholder","role","summary","title","value","style","xmlns"]),tt="http://www.w3.org/1998/Math/MathML",nt="http://www.w3.org/2000/svg",ot="http://www.w3.org/1999/xhtml";let rt=ot,it=!1,at=null;const lt=R({},[tt,nt,ot],g);let ct=R({},["mi","mo","mn","ms","mtext"]),st=R({},["annotation-xml"]);const ut=R({},["title","style","font","a","script"]);let...

performance. t = f[j+1] f[j+1] = fieldAdd(t, flen[j+1]) flen[j+1] = fieldMontgomeryMulSu(zeta, flen[j+1], t) } } } for i := range f { f[i] = fieldMontgomeryMul(f[i], 16382) // 16382 = 256⁻¹ * R mod q } return ringElement(f) } // nttMul multiplies two nttElements. func nttMul(a, b nttElement) (p nttElement) { for i := range p { p[i] = fieldMontgomeryMul(a[i], b[i]) } return p } // sampleNTT samples an nttElement uniformly at random from the seed rho and the // indices s and r. It implements Step 3 of...