ops::Placeholder(root.WithOpName("placeholder"), DT_FLOAT);
  Output const_0d = ops::Const(root.WithOpName("const_0d"), 42);

  Output const_2d = ops::Const(root.WithOpName("const_2d"), {{1, 2}, {4, 3}});

  EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42)));
  EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42), Name("const_0d")));

  EXPECT_THAT(const_2d.node(), NodeWith(ConstantValue({{1, 2}, {4, 3}})));

// CHECK-DAG: %[[CONST_2:.+]] = stablehlo.constant dense<3> : tensor<1xi32>
// CHECK-DAG: %[[CONST_3:.+]] = stablehlo.constant dense<4> : tensor<1xi32>
// CHECK-DAG: %[[CONST_4:.+]] = stablehlo.constant dense<2> : tensor<1xi32>

// CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<1x1x3x2xf32>}> : () -> tensor<1x1x3x2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = "tf.Const"() <{value = dense<-1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[CONST_2:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>

  %3 = stablehlo.add %2, %1 : tensor<1x3xf32>
  func.return %3: tensor<1x3xf32>
}
// CHECK: %[[CONST_0:.*]] = stablehlo.constant dense<2.000000e+00>
// CHECK: %[[CONST_1:.*]] = stablehlo.constant dense<2.000000e+00>
// CHECK: %[[XLA_CALL_MODULE:.*]] = "tf.XlaCallModule"(%arg0, %[[CONST_0]], %[[CONST_1]])
// CHECK: return %[[XLA_CALL_MODULE:.*]] : tensor<1x3xf32>
// CHECK: }

// CHECK-DAG: %[[CONST_0:.+]] = arith.constant dense<[1, 14, 14, 4]> : tensor<4xi32>
// CHECK-DAG: %[[CONST_1:.*]] = "tfl.pseudo_const"() <{value = dense<{{\[\[0, 0\], \[1, 1\], \[1, 1\], \[0, 0\]\]}}> : tensor<4x2xi32>}> : () -> tensor<4x2xi32>

  %2 = "tf.AddV2"(%0, %1): (tensor<15x28x28x1xf32>, tensor<1xf32>) -> tensor<15x28x28x1xf32>
  func.return %2 : tensor<15x28x28x1xf32>
// CHECK: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>

// CHECK: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<10xf32>}>
// CHECK: %[[PARTITIONEDCALL_0:.*]] = "tf.PartitionedCall"(%arg0, %arg1, %[[CONST_0]])
// CHECK-SAME: f = @composite_matmul_with_bias_and_relu6_fn_1}>
// CHECK-SAME: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[PARTITIONEDCALL_1:.*]] = "tf.PartitionedCall"(%arg0, %arg1, %[[CONST_0]])

    module.walk([&](mlir::arith::ConstantOp const_op) {
      MaybeUnfoldLargeSplatConstant(&op_builder, const_op);
    });
  }

 private:
  void MaybeUnfoldLargeSplatConstant(mlir::OpBuilder* op_builder,
                                     mlir::arith::ConstantOp const_op) const {
    auto splat_elements_attr =
        mlir::dyn_cast<SplatElementsAttr>(const_op.getValue());
    if (!splat_elements_attr) {
      return;

    // default.
    module.walk([&](mhlo::ConstantOp const_op) {
      UnfoldSplatConstant(&op_builder, const_op);
    });
  }

 private:
  void UnfoldSplatConstant(mlir::OpBuilder* op_builder,
                           mhlo::ConstantOp const_op) const {
    auto splat_elements_attr =
        mlir::dyn_cast<SplatElementsAttr>(const_op.getValue());
    if (!splat_elements_attr) {
      return;
    }

  %2 = stablehlo.add %1, %0 : tensor<1x4x3x3xf32>
  return %2 : tensor<1x4x3x3xf32>
}
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32>
// CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant
// CHECK-DAG: %[[TRANSPOSE_0:.+]] = stablehlo.transpose %[[CONST_0]], dims = [0, 2, 3, 1] : (tensor<1x4x3x3xf32>) -> tensor<1x3x3x4xf32>

// Check that the shape of the add is changed to reflect the deferred transpose.