// CHECK: stablehlo.constant dense<[2.000000e+00, 3.000000e+00]> : tensor<2xf32>

// -----

func.func @foldDivBothSplat() -> tensor<2xf32> {
  %0 = stablehlo.constant dense<4.0> : tensor<2xf32>
  %1 = stablehlo.constant dense<2.0> : tensor<2xf32>
  %2 = stablehlo.divide %0, %1 : tensor<2xf32>
  return %2 : tensor<2xf32>
}

// CHECK-LABEL: foldDivBothSplat
// CHECK: stablehlo.constant dense<2.000000e+00> : tensor<2xf32>

// -----

00000120  2b 01 92 8d f9 4f 5a 3a  53 11 fc 32 52 cc af cd  |+....OZ:S..2R...|
00000130  7b 94 0e 76 10 c2 16 36  2d a4 64 69 1c 05 70 20  |{..v...6-.di..p |
00000140  0d 23 cd 4a 33 c5 c7 db  db 0f f8 b6 42 0c 83 0a  |.#.J3.......B...|
00000150  a1 73 68 fb 87 2c 9d d2  d3 cf d7 3a bb 36 7e 83  |.sh..,.....:.6~.|
00000160  c3 3f bc e2 61 d9 c2 8b  15 a2 cc bf 14 a0 69 f4  |.?..a.........i.|
00000170  22 02 a9 ff 5e 55 37 6e  61 86 71 73 94 2f 7e 50  |"...^U7na.qs./~P|

  // CHECK-DAG: %[[LHS1:.*]] = mhlo.bitcast_convert %[[LHS]] : (tensor<3x2x!quant.uniform<i32:f32, 2.000000e+00:4>>) -> tensor<3x2xi32>
  // CHECK-DAG: %[[LHS2:.*]] = mhlo.bitcast_convert %[[LHS1]] : (tensor<3x2xi32>) -> tensor<3x2x!quant.uniform<i32:f32, 2.000000e+00:4>>
  %0 = "tf.UniformQuantize"(%input, %input_scales, %input_zps) {

  %10 = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
  %11 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
  %recurrent_input = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32>
  %cell_input = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32>

  // CHECK-DAG: %[[BIAS:.*]] = arith.constant dense<[-5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01,...

    %15, %16,
    %recurrent_stats, %cell_stats,
    %none, %20, %21, %22) ({}) {
      cell_clip = 5.000000e+01 : f32,
      effective_hidden_scale_intermediate = tensor<!quant.calibrated<f32<-5.000000e-01:5.000000e-01>>>,
      fused_activation_function = "TANH",
      input_to_cell_intermediate = tensor<!quant.calibrated<f32<-4.000000e+00:4.000000e+00>>>,
      input_to_forget_intermediate = tensor<!quant.calibrated<f32<-1.600000e+01:1.600000e+01>>>,

  %0 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %1 = "mhlo.pad"(%arg0, %0) <{edge_padding_high = dense<0> : tensor<3xi64>, edge_padding_low = dense<1> : tensor<3xi64>, interior_padding = dense<0> : tensor<3xi64>}> : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<11x11x11xf32>
  %2 = mhlo.constant dense<0.000000e+00> : tensor<f32>

// CHECK-SAME: effective_hidden_scale_intermediate = tensor<*x!quant.calibrated<f32<-5.000000e-01:5.000000e-01>>>
// CHECK-SAME: input_to_cell_intermediate = tensor<*x!quant.calibrated<f32<-4.000000e+00:4.000000e+00>>>
// CHECK-SAME: input_to_forget_intermediate = tensor<*x!quant.calibrated<f32<-1.600000e+01:1.600000e+01>>>
// CHECK-SAME: input_to_input_intermediate = tensor<*x!quant.calibrated<f32<-3.200000e+01:3.200000e+01>>>

// CHECK-NEXT: %[[Q:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x2x!quant.uniform<u8:f32, 1.000000e+00>>}> : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<u8:f32, 1.000000e+00>>
// CHECK-NEXT: %[[CST:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1x2x!quant.uniform<u8:f32, 1.000000e+00>>, value = dense<-76> : tensor<1x2xi8>}> : () -> tensor<1x2x!quant.uniform<u8:f32, 1.000000e+00>>

    %1 = stablehlo.constant dense<1.000000e+03> : tensor<1x1x1x1xf32>  // Input inverse scale.
    %2 = stablehlo.constant dense<-128> : tensor<1x1x1x1xi8>  // Input zero point.
    %3 = stablehlo.constant dense<2.000000e+01> : tensor<3x3x4x4xf32>  // Quantized filter tensor.
    %4 = stablehlo.constant dense<3.000000e+03> : tensor<1x1x1x4xf32>
    %5 = stablehlo.constant dense<4.000000e+03> : tensor<1x1x1x1xf32>  // Output inverse scale.