auto dx = internal::LeakyReluGrad(scope, grad_inputs[0], op.input(0),
                                    attrs.Alpha(alpha));
  grad_outputs->push_back(dx);
  return scope.status();
}
REGISTER_GRADIENT_OP("LeakyRelu", LeakyReluGradHelper);

Status LeakyReluGradGradHelper(const Scope& scope, const Operation& op,
                               const std::vector<Output>& grad_inputs,

    "UNPACK",
    "REDUCE_MIN",
    "FLOOR_DIV",
    "REDUCE_ANY",
    "SQUARE",
    "ZEROS_LIKE",
    "FILL",
    "FLOOR_MOD",
    "RANGE",
    "RESIZE_NEAREST_NEIGHBOR",
    "LEAKY_RELU",
    "SQUARED_DIFFERENCE",
    "MIRROR_PAD",
    "ABS",
    "SPLIT_V",
    "UNIQUE",
    "CEIL",
    "REVERSE_V2",
    "ADD_N",
    "GATHER_ND",
    "COS",
    "WHERE",

      "HSVToRGB",
      "IFFT",
      "IFFT2D",
      "IFFT3D",
      "IRFFT",
      "IRFFT2D",
      "IRFFT3D",
      "If",
      "InTopKV2",
      "L2Loss",
      "LeakyRelu",
      "LinSpace",
      "ListDiff",
      "LogMatrixDeterminant",
      "LowerBound",
      "MatMul",
      "MatrixBandPart",
      "MatrixDiag",
      "MatrixDiagPart",

  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

  let summary = [{
Computes rectified linear gradients for a LeakyRelu operation.
  }];

  let arguments = (ins
    Arg<TF_FloatTensor, [{The backpropagated gradients to the corresponding LeakyRelu operation.}]>:$gradients,
    Arg<TF_FloatTensor, [{The features passed as input to the corresponding LeakyRelu operation,
OR the outputs of that operation (both work equivalently).}]>:$features,

        op, input_ty, concat, slice_begin_indices,
        rewriter.getI64TensorAttr(input_shape));
    return success();
  }
};

/// Converts a TF::LeakyReluOp to HLO.
/// LeakyRelu(x) = alpha * x if x < 0 else x.
class ConvertLeakyReluOp : public OpRewritePattern<TF::LeakyReluOp> {
 public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(TF::LeakyReluOp op,