%w = arith.constant dense<127.0> : tensor<4x12xf32>
  %b = arith.constant dense<0.0> : tensor<4xf32>
  %fc = "tfl.fully_connected"(%arg0, %w, %b) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<4x12xf32>, tensor<4xf32>) -> tensor<1x112x112x4xf32>
  func.return %fc : tensor<1x112x112x4xf32>

// CHECK:           %[[VAL_14:.*]] = "tfl.reshape"(%[[VAL_13]], %[[VAL_8]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x1x384x128xf32>, tensor<2xi32>) -> tensor<384x128xf32>
// CHECK:           %[[VAL_15:.*]] = "tfl.reshape"(%[[VAL_14]], %[[VAL_5]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<384x128xf32>, tensor<3xi32>) -> tensor<1x384x128xf32>

  func.return %0 : tensor<8x16xf32>

// CHECK-LABEL:minimum
// CHECK:  "tfl.minimum"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32>
}

func.func @realDiv(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xf32> {
  %0 = "tf.RealDiv"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32>

  %0 = "tf.MatMul"(%input, %filter_new) {
  } : (tensor<8x9xf32>, tensor<9x10xf32>) -> tensor<8x10xf32>
  return %0 : tensor<8x10xf32>
})mlir";
  constexpr absl::string_view kProgram = R"mlir(
func.func @main(%input: tensor<8x9xf32>, %filter: tensor<9x10xi8>) -> tensor<8x10xf32> {
  %filter_scale = "tf.Const"() { value = dense<0.0235> : tensor<f32> } : ()
  -> tensor<f32>

    // CHECK: (tensor<!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<128xf32>
    %3 = "tf.Restore"(%arg0, %cst_0) {device = ""} : (tensor<!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<*xf32>
    "tf.AssignVariableOp"(%2, %3) : (tensor<!tf_type.resource<tensor<128xf32>>>, tensor<*xf32>) -> ()

// CHECK:}

func.func @reshape(%arg0 : tensor<1x128xi32>) -> tensor<4x32x1xi32>{
  %0 = "vhlo.reshape_v1"(%arg0) : (tensor<1x128xi32>) -> tensor<4x32x1xi32>
  func.return %0 : tensor<4x32x1xi32>
}

//CHECK:func.func private @reshape(%arg0: tensor<1x128xi32>) -> tensor<4x32x1xi32> {
//CHECK-NEXT: %0 = "vhlo.reshape_v1"(%arg0) : (tensor<1x128xi32>) -> tensor<4x32x1xi32>
//CHECK-NEXT: return %0 : tensor<4x32x1xi32>

  %0 = mhlo.reshape %arg1 : (tensor<1x72x128xf32>) -> tensor<72x128xf32>
  %1 = "mhlo.dot_general"(%arg0, %0) {
    dot_dimension_numbers = #mhlo.dot<
      lhs_contracting_dimensions = [2],
      rhs_contracting_dimensions = [0]
    >} : (tensor<1x72x72xf32>, tensor<72x128xf32>) -> tensor<1x72x128xf32>
  func.return %1 : tensor<1x72x128xf32>

}

func.func @matmulNoTransposeAOrB(%arg0: tensor<1x1280xf32>, %arg1: tensor<1280x1000xf32>) -> tensor<1x1000xf32> {
  %166 = "tf.MatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", _output_shapes = ["tfshape$dim { size = 1} dim { size = 1000}"], device = "", name = "matmul", transpose_a = false, transpose_b = false} : (tensor<1x1280xf32>, tensor<1280x1000xf32>) -> tensor<1x1000xf32>
  func.return %166 : tensor<1x1000xf32>

// CHECK-SAME: (tensor<1x37xf32>, tensor<40x37xf32>, none) -> tensor<1x40xf32>
  func.return %1 : tensor<1x40xf32>
}

// CHECK-LABEL: RemoveLstmQuantZeroBias
func.func @RemoveLstmQuantZeroBias(
  %arg0: tensor<1x528xf32>,
  %arg1: tensor<2048x528xf32>,
  %arg2: tensor<2048x528xf32>,
  %arg3: tensor<2048x528xf32>,
  %arg4: tensor<2048x528xf32>,
  %arg5: tensor<2048x640xf32>,
  %arg6: tensor<2048x640xf32>,

    func.return %0 : tensor<4x128xf32>
  }
  func.func @_func(%arg0: tensor<4x128xf32>) -> tensor<4x128xf32> {
    func.return %arg0 : tensor<4x128xf32>
  }