tensor_proto.set_dtype(DT_FLOAT);
      tensor_proto.add_float_val(5.0f);
      tensor_proto.add_float_val(6.0f);
      tensor_proto.add_float_val(7.0f);
      tensor_proto.add_float_val(8.0f);
      tensor_proto.add_float_val(9.0f);
      tensor_proto.add_float_val(10.0f);
      return tensor_proto;
    }
  };

  auto my_create_func = [](TF_OpKernelConstruction* ctx) {

      TFL_AF_Relu6),
     TFL_AF_None),
    (Arith_ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "0.166666666f">),
    TFL_AF_None),
  (TFL_HardSwishOp $x)>;

def MatchHardSwishPattern4 : Pat<
  (TFL_MulOp
    (TFL_MulOp
     (TFL_AddOp
      $x,
      (Arith_ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "3.0f">),
      TFL_AF_Relu6),

      ops::Const(scope.WithOpName("ValueToAssign" + id), 1.0f);
  ops::AssignVariableOp assign_op(scope.WithOpName("Assignment" + id),
                                  var_handle, value_to_write);
  return assign_op.operation.node();
}

Node* MakeNeutral(const Scope& scope, const string& id) {
  return ops::Const(scope.WithOpName("Const" + id), 42.0f).node();
}
}  // namespace

  float result[100 * 100] = {0};
  EXPECT_EQ(sizeof(result), TF_TensorByteSize(t));
  memcpy(&result[0], TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  for (int i = 0; i < 100 * 100; ++i) {
    EXPECT_EQ(2.0f, result[i]);
  }
  TFE_DeleteContext(ctx);
  TF_DeleteStatus(status);
#endif  // PLATFORM_WINDOWS
}
TEST(CAPI, ExecuteAdd) {
  ExecuteAdd(
      /*async=*/false,
      /*forward_input*/ false,

	{"%#g", -1.0, "-1.00000"},
	{"%#g", 1.1, "1.10000"},
	{"%#g", 123456.0, "123456."},
	{"%#g", 1234567.0, "1.234567e+06"},
	{"%#g", 1230000.0, "1.23000e+06"},
	{"%#g", 1000000.0, "1.00000e+06"},
	{"%#.0f", 1.0, "1."},
	{"%#.0e", 1.0, "1.e+00"},
	{"%#.0x", 1.0, "0x1.p+00"},
	{"%#.0g", 1.0, "1."},
	{"%#.0g", 1100000.0, "1.e+06"},
	{"%#.4f", 1.0, "1.0000"},
	{"%#.4e", 1.0, "1.0000e+00"},

  ASSERT_EQ(4, TF_TensorByteSize(t));
  float* p = static_cast<float*>(TF_TensorData(t));
  *f = *p;
}

TEST_F(CApiGradientsTest, MultipleCallsToAddGradients) {
  const float X = 3.0f, Y = 7.0f;
  TF_Operation* x = Placeholder(graph_, s_, "x", TF_FLOAT);
  TF_Operation* y = Placeholder(graph_, s_, "y", TF_FLOAT);
  TF_Operation* xy = Mul(x, y, graph_, s_, "xy");
  TF_Output dxy_dx, dxy_dy;

  ASSERT_THAT(*float_output_quant_params->max(), SizeIs(1));

  ASSERT_THAT(output_quant_params->scale, SizeIs(1));
  ASSERT_THAT(output_quant_params->zero_point, SizeIs(1));
  ASSERT_THAT(1.0f / 256.0f, Eq(output_quant_params->scale[0]));
  ASSERT_THAT(-128, Eq(output_quant_params->zero_point[0]));

  // check op and versioning.
  EXPECT_THAT(model_.operator_codes, SizeIs(1));

΋箞�1o6���AZO7^g�J�@��Pg�s�yU8(��|��2+��
�^2'U:��o�si�h	�ΏMdB�[zӈ...

// dynamic batch dimension is properly quantized.

// Note that this checks for identical condition as
// quantize_conv_with_bias_dynamic_fn, omitting stablehlo.maximum.
// This is because activation clipping which includes 0.0f can be simply
// omitted from the graph as the lifted function's out_scale and out_zp are
// already calculated based on the clipped distribution.
// Note that the resulting scale and zero point should be calculated based on

                String str = threadConfiguration.substring(0, threadConfiguration.length() - 1);
                float coreMultiplier = Float.parseFloat(str);

                if (coreMultiplier <= 0.0f) {
                    throw new IllegalArgumentException("Invalid threads core multiplier value: '" + threadConfiguration
                            + "'. Value must be positive.");
                }