func.return %0 : tensor<1x2x3x4x5x6x7x8xi32>
}

func.func @LeakyRelu(%arg0: tensor<1xf32>) -> tensor<1xf32> {
  %2 = "tf.LeakyRelu"(%arg0) {alpha = 0.1 : f32} : (tensor<1xf32>) -> tensor<1xf32>
  func.return %2: tensor<1xf32>

// CHECK-LABEL: LeakyRelu
// CHECK:  "tfl.leaky_relu"(%arg0) <{alpha = 1.000000e-01 : f32}> : (tensor<1xf32>) -> tensor<1xf32>
}

OpFoldResult LeakyReluOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  assert(operands.size() == 1 && "leaky relu has one operand");

  // leaky_relu(x, alpha: 1) -> x
  if (getAlpha().convertToFloat() == 1.0f &&
      getOperand().getType() == getType())
    return getOperand();

  auto calculate = [&](FloatAttr arg) {
    APFloat val = arg.getValue();

    return quant::GetFixedOutputRange(is_signed, bit_width, result_type,
        /*scale=*/1.0 / (1<<(bit_width-1)), /*zero_point=*/0);
  }
  }];
}

def TFL_LeakyReluOp: TFL_Op<"leaky_relu", [
    SameOperandsAndResultShape,
    QuantizableResult,
    Pure,
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>]> {

  func.return %0 : tensor<16x16x16x16xf32>
}

// -----

// Test valid tf.LeakyRelu
// CHECK-LABEL: func @testLeakyRelu(%arg0: tensor<16xf32>)
func.func @testLeakyRelu(tensor<16xf32>) -> tensor<16xf32> {
^bb0(%arg0: tensor<16xf32>):
  %0 = "tf.LeakyRelu"(%arg0) {alpha = 0.2 : f32} : (tensor<16xf32>) -> tensor<16xf32>
  func.return %0 : tensor<16xf32>
}

// -----

  func.return %0: tensor<*xf32>
}

// CHECK-LABEL: func @testLeakyRelu
func.func @testLeakyRelu(%arg0 : tensor<16xf32>) -> (tensor<16xf32>) {
  %2 = "tf.LeakyRelu"(%arg0) {alpha = 1.0 : f32} : (tensor<16xf32>) -> tensor<16xf32>
  // CHECK: return %arg0
  func.return %2 : tensor<16xf32>
}

// CHECK-LABEL: testSameBitcastType