%fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%r1, %mini, %maxi) {num_bits = 5, narrow_range = false} : (tensor<3x3x48xf32>, tensor<48xf32>, tensor<48xf32>) -> tensor<3x3x48xf32>
  %r2 = "tf.Reshape"(%fq, %s2) {T = f32, Tshape = i32, device = ""} : (tensor<3x3x48xf32>, tensor<4xi32>) -> tensor<3x3x48x1xf32>

    %7 = stablehlo.broadcast_in_dim %6, dims = [2, 1] : (tensor<1x3xf32>) -> tensor<2x3x2xf32>
    %8 = "quantfork.qcast"(%7) {volatile} : (tensor<2x3x2xf32>) -> tensor<2x3x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>
    %9 = "quantfork.dcast"(%8) : (tensor<2x3x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<2x3x2xf32>
    return %9 : tensor<2x3x2xf32>
  }

  // CHECK: quantized_dot_general_fn_1

  %fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%r1, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<3x3x48xf32>, tensor<48xf32>, tensor<48xf32>) -> tensor<3x3x48xf32>
  %r2 = "tf.Reshape"(%fq, %s2) {T = f32, Tshape = i32, device = ""} : (tensor<3x3x48xf32>, tensor<4xi32>) -> tensor<3x3x48x1xf32>

func.func @ConvertIdentityGatherNdOp3D(%arg0: tensor<4x3x4xf32>) -> tensor<4x3x4xf32> {
  %cst = arith.constant dense<[[0], [1], [2], [3]]> : tensor<4x1xi32>
  %0 = "tfl.gather_nd"(%arg0, %cst) : (tensor<4x3x4xf32>, tensor<4x1xi32>) -> tensor<4x3x4xf32>
  func.return %0 : tensor<4x3x4xf32>

// CHECK-LABEL: ConvertIdentityGatherNdOp3D
// CHECK-SAME: (%[[ARG:.*]]: tensor<4x3x4xf32>) -> tensor<4x3x4xf32>

// CHECK:           %12 = "tf.Reshape"(%0, %11) : (tensor<2x?x3x4xf32>, tensor<4xi32>) -> tensor<2x?x3x4xf32>
// CHECK:           %13 = "tf.BatchMatMulV3"(%6, %12) <{adj_x = false, adj_y = false, grad_x = false, grad_y = false}> : (tensor<2x?x2x3xf32>, tensor<2x?x3x4xf32>) -> tensor<2x?x2x4xf32>
// CHECK:           %14 = "tf.Shape"(%arg0) : (tensor<2x?x2x3xf32>) -> tensor<4xi32>

func.func @leaky_relu_grad(%arg0: tensor<1x4x4xf32>, %arg1: tensor<1x4x4xf32>) -> tensor<1x4x4xf32> attributes {tf.entry_function = {}} {
    // CHECK-NEXT: %[[ALPHA:.*]] = "chlo.constant_like"(%arg1) <{value = 2.000000e-01 : f32}> : (tensor<1x4x4xf32>) -> tensor<1x4x4xf32>
    // CHECK-NEXT: %[[ZERO:.*]] = "chlo.constant_like"(%arg1) <{value = 0.000000e+00 : f32}> : (tensor<1x4x4xf32>) -> tensor<1x4x4xf32>

func.func @testTranspose(tensor<2x3x4xf32>) -> tensor<3x2x4xf32> {
^bb0(%arg0: tensor<2x3x4xf32>):
  %cst = arith.constant dense<[2, 0, 1]> : tensor<3xi32>
  // expected-error @+1 {{requires y.shape[0] (3) to be equal to x.shape[perm[2]] (4)}}
  %0 = "tf.Transpose"(%arg0, %cst) {T = "tfdtype$DT_FLOAT", Tperm = "tfdtype$DT_INT32"} : (tensor<2x3x4xf32>, tensor<3xi32>) -> tensor<3x2x4xf32>
  func.return %0 : tensor<3x2x4xf32>
}

// -----

// 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

// -----

func.func private @dense_image_warp_invalid_input_shape(%arg0: tensor<2x4x4xf32>, %arg1: tensor<2x4x4x2xf32>) -> tensor<2x4x4x1xf32> attributes {tf._implements = "addons:DenseImageWarp"}

// expected-warning @+1 {{Flow should be a 4D float tensor}}
func.func private @dense_image_warp_invalid_flow_shape(%arg0: tensor<2x4x4x1xf32>, %arg1: tensor<2x4x4xf32>) -> tensor<2x4x4x1xf32> attributes {tf._implements = "addons:DenseImageWarp"}

  %arg0_shape = "tf.Const"() {value = dense<[1, 8, 4]> : tensor<3xi32>} : () -> tensor<3xi32>
  %arg0_reshaped = "tf.Reshape"(%arg0, %arg0_shape) : (tensor<8x4xf32>, tensor<3xi32>) -> tensor<1x8x4xf32>
  %zeroi2 = "tf.Const"() {value = dense<0> : tensor<2xi32>} : () -> tensor<2xi32>
  %axis = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>