: (tensor<?x3x2x1xf32>, tensor<2x1x1x1xf32>) -> tensor<?x2x2x1xf32>
  %1 = chlo.broadcast_add %0, %zp_offset : (
      tensor<?x2x2x1xf32>, tensor<?x2x2x1xf32>) -> tensor<?x2x2x1xf32>
  %2 = chlo.broadcast_add %1, %bias : (
      tensor<?x2x2x1xf32>, tensor<1xf32>) ->tensor<?x2x2x1xf32>
  return %2 : tensor<?x2x2x1xf32>
}

// -----

// CHECK-LABEL: func @dot_general_add_add

// CHECK-NEXT:    return %2 : tensor<3x2xf32>
}

func.func @transpose_3d(%arg0: tensor<1x2x3xf32>) -> tensor<3x2x1xf32> {
  %0 = "mhlo.transpose"(%arg0) <{permutation = dense<[2, 1, 0]> : tensor<3xi64>}> : (tensor<1x2x3xf32>) -> tensor<3x2x1xf32>
  func.return %0 : tensor<3x2x1xf32>

// CHECK-LABEL:   transpose_3d
// CHECK-NEXT:    %0 = "tfl.pseudo_const"() <{value = dense<[2, 1, 0]> : tensor<3xi64>}> : () -> tensor<3xi64>

// CHECK: %[[v0:.*]] = "tf.Reshape"(%arg0, %[[cst]]) : (tensor<2x1x1x11xf32>, tensor<3xi64>) -> tensor<2x1x11xf32>
// CHECK: %[[v1:.*]] = "tf.BatchMatMulV2"(%[[v0]], %arg1) <{adj_x = false, adj_y = false}> : (tensor<2x1x11xf32>, tensor<2x11x2xf32>) -> tensor<2x1x2xf32>
// CHECK: %[[v2:.*]] = "tf.Reshape"(%[[v1]], %[[cst_1]]) : (tensor<2x1x2xf32>, tensor<4xi64>) -> tensor<2x1x1x2xf32>

}

// -----

func.func @trivial_dynamic_update_slice(%arg0: tensor<2x7x14xf32>, %arg1: tensor<2x7x14xf32>) -> tensor<2x7x14xf32> {
  %0 = arith.constant dense<0> : tensor<3xi32>
  %1 = "tfl.dynamic_update_slice"(%arg0, %arg1, %0) : (tensor<2x7x14xf32>, tensor<2x7x14xf32>, tensor<3xi32>) -> tensor<2x7x14xf32>
  // CHECK: return %arg1
  func.return %1 : tensor<2x7x14xf32>
}

// -----

  module {
    func.func @main(%arg0: tensor<2x2x2xf32>, %arg1: tensor<2x2x2xf32>) -> tensor<2x2x2xf32> attributes {_from_xla_call_module} {
      %0 = stablehlo.dot_general %arg0, %arg1,
        batching_dims = [0] x [0],
        contracting_dims = [2] x [1],
        precision = [DEFAULT, DEFAULT]
      : (tensor<2x2x2xf32>, tensor<2x2x2xf32>) -> tensor<2x2x2xf32>
        return %0 : tensor<2x2x2xf32>
    }
  }
)mlir";

// CHECK: Number of dequantize layers added: 1
}


// -----

module {
  func.func @float_einsum(%arg0: tensor<?x64x32xf32>, %arg1: tensor<32x2x16xf32>) -> (tensor<?x64x2x16xf32>) {
    %0 = "tf.Einsum"(%arg0, %arg1) {equation = "abc,cde->abde"} : (tensor<?x64x32xf32>, tensor<32x2x16xf32>) -> tensor<?x64x2x16xf32>
    func.return %0 : tensor<?x64x2x16xf32>
  }

// CHECK-LABEL: func @float_einsum

  >} : (tensor<2x1x512xf32>, tensor<512x13xf32>) -> tensor<2x1x13xf32>
  %2 = "mhlo.dot_general"(%arg2, %arg3) {
    dot_dimension_numbers = #mhlo.dot<
      lhs_contracting_dimensions = [2],
      rhs_contracting_dimensions = [0]
  >} : (tensor<3x1x512xf32>, tensor<512x13xf32>) -> tensor<3x1x13xf32>
  %r = "mhlo.concatenate"(%0, %1, %2) <{dimension = 0 : i64}> : (tensor<1x1x13xf32>, tensor<2x1x13xf32>, tensor<3x1x13xf32>) -> tensor<6x1x13xf32>

  %1 = "tfl.add"(%cst_2, %cst_1) {fused_activation_function = "NONE"} : (tensor<2x2x2xf32>, tensor<  2x2xf32>) -> tensor<2x2x2xf32>
  %2 = "tfl.add"(%cst_0, %cst_2) {fused_activation_function = "NONE"} : (tensor<    2xf32>, tensor<2x2x2xf32>) -> tensor<2x2x2xf32>

  func.return %0, %1, %2 : tensor<2x2xf32>, tensor<2x2x2xf32>, tensor<2x2x2xf32>