TAC_REGISTER_GPU_OP(CosOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(DivOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(ExpOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(HardSwishOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(LogOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(LogisticOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(MaxPool2DOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(MirrorPadOp, CreateBasicOpNoCost);

     * can narrow the possible floor(log10(x)) values to two. For example, if floor(log2(x)) is 6,
     * then 64 <= x < 128, so floor(log10(x)) is either 1 or 2.
     */
    int y = maxLog10ForLeadingZeros[Long.numberOfLeadingZeros(x)];
    /*
     * y is the higher of the two possible values of floor(log10(x)). If x < 10^y, then we want the

                        logger.error "Done with " + projectName
                    }
                }
            }
        """

        when:
        server.expectConcurrent("log1", "log2", "log3")
        executer.withArgument("--parallel")
        // run build in another process to avoid interference from logging from test fixtures
        result = executer.withTasks("log").start().waitForFinish()

    }
  }];
}

def TF_Log1pOp : TF_Op<"Log1p", [Pure, TF_CwiseUnary, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes natural logarithm of (1 + x) element-wise.";

  let description = [{
I.e., \\(y = \log_e (1 + x)\\).

Example:

```python
x = tf.constant([0, 0.5, 1, 5])
tf.math.log1p(x) ==> [0., 0.4054651, 0.6931472, 1.7917595]
```
  }];

      (TF_TensorScatterAddOp
       (TF_FillOp $shape, (TF_ConstOp (GetScalarOfType<0> $updates))),
       $indices, $updates)>;

//===----------------------------------------------------------------------===//
// Xdivy, Xlog1p and Xlogy op patterns.
//===----------------------------------------------------------------------===//

class BinaryXopyPat<dag From, dag To> : Pat<
  From,
  (TF_SelectV2Op
    (TF_EqualOp
      $x,

        TypeID::get<TF::LeftShiftOp>(),
        TypeID::get<TF::LessOp>(),
        TypeID::get<TF::ListDiffOp>(),
        TypeID::get<TF::LogicalAndOp>(),
        TypeID::get<TF::LogicalNotOp>(),
        TypeID::get<TF::LogOp>(),
        TypeID::get<TF::LowerBoundOp>(),
        TypeID::get<TF::MakeUniqueOp>(),
        TypeID::get<TF::MatMulOp>(),
        TypeID::get<TF::MatrixDiagV3Op>(),
        TypeID::get<TF::MatrixInverseOp>(),

		{"J0", Func, 0},
		{"J1", Func, 0},
		{"Jn", Func, 0},
		{"Ldexp", Func, 0},
		{"Lgamma", Func, 0},
		{"Ln10", Const, 0},
		{"Ln2", Const, 0},
		{"Log", Func, 0},
		{"Log10", Func, 0},
		{"Log10E", Const, 0},
		{"Log1p", Func, 0},
		{"Log2", Func, 0},
		{"Log2E", Const, 0},
		{"Logb", Func, 0},
		{"Max", Func, 0},
		{"MaxFloat32", Const, 0},
		{"MaxFloat64", Const, 0},

(Div16 <t> n (Const16 [c])) && isPowerOfTwo16(c) =>
  (Rsh16x64
    (Add16 <t> n (Rsh16Ux64 <t> (Rsh16x64 <t> n (Const64 <typ.UInt64> [15])) (Const64 <typ.UInt64> [int64(16-log16(c))])))
    (Const64 <typ.UInt64> [int64(log16(c))]))
(Div32 <t> n (Const32 [c])) && isPowerOfTwo32(c) =>
  (Rsh32x64
    (Add32 <t> n (Rsh32Ux64 <t> (Rsh32x64 <t> n (Const64 <typ.UInt64> [31])) (Const64 <typ.UInt64> [int64(32-log32(c))])))

        `tf.math.greater`, `tf.math.greater_equal`, `tf.math.igamma`,
        `tf.math.igammac`, `tf.math.invert_permutation`, `tf.math.less`,
        `tf.math.less_equal`, `tf.math.lgamma`, `tf.math.log`, `tf.math.log1p`,
        `tf.math.logical_and`, `tf.math.logical_not`, `tf.math.logical_or`,
        `tf.math.maximum`, `tf.math.minimum`, `tf.math.not_equal`,
        `tf.math.polygamma`, `tf.math.reciprocal`, `tf.math.rint`,

  };
  return ConstFoldUnaryOp(result_type, operands[0], compute);
}

//===----------------------------------------------------------------------===//
// LogOp
//===----------------------------------------------------------------------===//

OpFoldResult LogOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  Type result_type = getType();
  // Only constant fold for tensor of f32 is implemented.