%0 = "tfl.dequantize"(%arg0) : (tensor<1000x1000x!quant.uniform<i8:f32, 7.812500e-03>>) -> tensor<1000x1000xf32>
  %1 = "tfl.concatenation"(%0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1000x1000xf32>, tensor<1000x1000xf32>) -> tensor<2000x1000xf32>
  func.return %1 : tensor<2000x1000xf32>

// CHECK-NEXT: tfl.dequantize

// CHECK-LABEL: @unfold_large_constant_splat
func.func @unfold_large_constant_splat() -> (tensor<10x10xf32>, tensor<1000x1000xf32>) {
  %0 = arith.constant dense<0.00000e+00> : tensor<10x10xf32>
  %1 = arith.constant dense<1.00000e+00> : tensor<1000x1000xf32>
  func.return %0, %1 : tensor<10x10xf32>, tensor<1000x1000xf32>

  // CHECK-DAG: %cst = arith.constant dense<0.000000e+00> : tensor<10x10xf32>

func.func @testReshape(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>, %arg2: tensor<10000xf32>, %arg3: tensor<*xi32>) -> (tensor<100x100xf32>, tensor<*xf32>, tensor<100x100xf32>, tensor<100x100xf32>, tensor<*xf32>, tensor<*xf32>) {
  %shape1 = arith.constant dense<100> : tensor<2xi32>
  %r1 = "tf.Reshape" (%arg0, %shape1) : (tensor<*xf32>, tensor<2xi32>) -> tensor<100x100xf32>
  %shape2 = "tf.Shape"(%arg0) : (tensor<*xf32>) -> tensor<?xi32>

}

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>

    %0 = "tf.Const"() {value = dense<10000000> : tensor<1xi32>} : () -> tensor<1xi32>
    // CHECK-NOT: tf.ParameterizedTruncatedNormal
    %1 = "tf.ParameterizedTruncatedNormal"(%0, %arg0, %arg1, %arg2, %arg3) {seed = 0 : i64, seed2 = 0 : i64} : (tensor<1xi32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>) -> tensor<10000000xf32>
    func.return %1 : tensor<10000000xf32>
  }