func.func @CheckNumericVerifyWholeModelNoQuantizeOps(%arg0: tensor<?x5x5x2xf32>) -> (tensor<?x1x1x3xf32>) {
  %0 = "quantfork.stats"(%arg0) {
    layerStats = dense<[0.0, 1.0]> : tensor<2xf32>
  } : (tensor<?x5x5x2xf32>) -> tensor<?x5x5x2xf32>
  %1 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<3x5x5x2xf32>} : () -> tensor<3x5x5x2xf32>

  func.return %mm_s : tensor<1x3x3x2xf32>

// CHECK: %[[w:.*]] = arith.constant dense<1.270000e+02> : tensor<512x2xf32>
// CHECK: %[[q_w:.*]] = "tfl.quantize"(%[[w]]) <{qtype = tensor<512x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>}>
// CHECK: %[[dq_w:.*]] = "tfl.dequantize"(%[[q_w]]) : (tensor<512x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<512x2xf32>

// -----

// Tests that a float `stablehlo.transpose` is not converted to `tfl.transpose`.

func.func @transpose_float(%arg0: tensor<2x3x4xf32>) -> tensor<4x3x2xf32> {
  %0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<2x3x4xf32>) -> tensor<4x3x2xf32>
  return %0 : tensor<4x3x2xf32>
}
// CHECK-LABEL: transpose_float
// CHECK-NOT: tfl.transpose
// CHECK: stablehlo.transpose

// -----

    rhs_quantization_min_val = -128 : i64,
    rhs_quantization_max_val = 127 : i64
  } : (tensor<1x2x2x3xf32>, tensor<2x3x3x2x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<1x3x3x2xf32>
  func.return %0 : tensor<1x3x3x2xf32>
}

//===----------------------------------------------------------------------===//
// tf.UniformQuantize and tf.UniformDequantize legalization

  %2 = "tf.SpaceToBatchND"(%arg0, %0, %1) : (tensor<1x8x2xf32>, tensor<1xi32>, tensor<1x2xi32>) -> tensor<3x5x2xf32>

  // CHECK: return [[VAL8]]
  func.return %2 : tensor<3x5x2xf32>
}

// CHECK-LABEL: avoid_lowering_space_to_batch_nd
func.func @avoid_lowering_space_to_batch_nd(%arg0: tensor<1x8x2xf32>, %arg1: tensor<*xi32>) -> (tensor<3x5x2xf32>) {
  %0 = "tf.Const"() {value = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>

func.func @testBiasAdd(%arg0: tensor<2x3x5x7xf32>, %arg1: tensor<5x7xf32>) -> tensor<2x3x5x7xf32> {
  // expected-error @+1 {{requires bias operand to have rank exactly one}}
  %0 = "tf.BiasAdd"(%arg0, %arg1) {data_format = "NHWC"} : (tensor<2x3x5x7xf32>, tensor<5x7xf32>) -> tensor<2x3x5x7xf32>
  func.return %0 : tensor<2x3x5x7xf32>
}

// -----

func.func @testBiasAdd(%arg0: tensor<2x3x5x7xf32>, %arg1: tensor<5xf32>) -> tensor<2x3x5x7xf32> {

}

func.func @gatherV2VectorIndices_I64Axis(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> tensor<1x3x5x20xf32> {
  %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi64> } : () -> tensor<1xi64>
  %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi64>) -> tensor<1x3x5x20xf32>
  func.return %1 : tensor<1x3x5x20xf32>

func.func @testRankOfRankedTensorUnrankedOutput(%arg0 : tensor<4x3x2xf32>) -> tensor<*xi32> {
  // Regression test to make sure we don't crash in this case.
  %0 = "tf.Rank"(%arg0) : (tensor<4x3x2xf32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}

// CHECK-LABEL: testRankOfRankedTensorDynamicShapeOutput
func.func @testRankOfRankedTensorDynamicShapeOutput(%arg0 : tensor<4x3x2xf32>) -> tensor<?xi32> {

  func.return %rst : tensor<256x8x7x4xf32>

// LOBIT-DAG: %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<4xf32>
// LOBIT-DAG: %[[CONSTANT0:.*]] = arith.constant dense<0.000000e+00> : tensor<4x3x3x3xf32>
// LOBIT: %[[QUANTIZE:.*]] = "tfl.quantize"(%[[CONSTANT0]]) <{qtype = tensor<4x3x3x3x!quant.uniform<u4<1:15>:f32:0, {1.000000e+00:1,1.000000e+00:2,1.000000e+00:7,1.000000e+00:15}>>}>

  %cst1 = arith.constant dense<2.0> : tensor<2xf32>
  %cst2 = arith.constant dense<[[[[1.0, 2.0]]]]> : tensor<1x1x1x2xf32>