```mlir
      %0 = "tf.Const"() {value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %1 = "tf.Const"() {device = "", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
    ```

    then running this pass with 'default-device=foobar', we get:

    ```mlir

  %2 = mhlo.constant dense<6.400000e+01> : tensor<64xf32>
  %3 = mhlo.constant dense<3.200000e+01> : tensor<64xf32>
  %4 = mhlo.constant dense<5.000000e-01> : tensor<64xf32>
  %5 = "mhlo.iota"() <{iota_dimension = 0 : i64}> : () -> tensor<64xf32>
  %6 = mhlo.add %5, %4 : tensor<64xf32>
  %7 = mhlo.multiply %6, %3 : tensor<64xf32>
  %8 = mhlo.divide %7, %2 : tensor<64xf32>
  %9 = mhlo.floor %8 : tensor<64xf32>

func.func @dot_general_with_bias_same_shape_fn(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<2x3xf32>
  %1 = stablehlo.constant dense<2.000000e+00> : tensor<1x3xf32>
  %2 = stablehlo.dot_general %arg0, %0, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>

  return {};
}

// Convert Pack to Reshape when there is only one operand to be packed.
// For example,
//
//   %0 = tf.Pack(%input) {axis = 0} // %input : tensor<2x3xf32>
//
// can be canonicalized to
//
//   %shape = "tf.Const"() {value = dense<[1, 2, 3]> : tensor<3xi64>}
//   %0 = tf.Reshape(%input, %shape)
struct ConvertPackToReshape : public OpRewritePattern<PackOp> {