// CHECK-SAME:      <{cell_clip = 1.000000e+01 : f32, fused_activation_function = "TANH", kernel_type = #tfl<lstm_kernel_type_attr FULL>, proj_clip = 0.000000e+00 : f32}> ({

^bb0(%arg0: tensor<? x f32>):
  // CHECK: "tfl.tanh"(%arg0)
  %0 = "tfl.tanh"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// CHECK-LABEL: testTanhWithQI8
func.func @testTanhWithQI8(%arg0: tensor<? x !quant.uniform<i8:f32, 0.1>>) -> tensor<? x !quant.uniform<i8:f32, 0.1>> {

  // CHECK:  return
}

func.func @Tanh(%arg0: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tf.Tanh"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
  func.return %0: tensor<1xf32>

// CHECK-LABEL: Tanh
// CHECK:  "tfl.tanh"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
}

func.func @cast(%arg0: tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> {

int64_t LogisticOp::GetArithmeticCount(Operation* op) {
  int64_t count;
  // As a very rough ballpark, the cost of evaluating a math function
  // such as tanh or logistic is about 32 multiplications, and about as
  // many additions/subtractions. (Just a power-of-two order-of-magnitude
  // from looking at actual implementations that we use in runtime/code).

    }
    void $cppClass::print(OpAsmPrinter &p) {
      return printOneResultOp(getOperation(), p);
    }
  }];
}

def TFL_TanhOp: TFL_Op<"tanh", [
    Pure,
    SameOperandsAndResultShape,
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    FixedOutputRangeInterface,
    QuantizableResult,

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