// CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"([[EXPAND]], [[FILTER]]) <{dilations = [1, 1, 2, 1], padding = "SAME", strides = [1, 1, 1, 1]}> : (tensor<1x1x128x3xf32>, tensor<1x5x3x8xf32>) -> tensor<1x1x128x8xf32>
  // CHECK-NEXT: [[RESULT:%.*]] = "tf.Squeeze"([[CONV]]) <{squeeze_dims = [-3]}> : (tensor<1x1x128x8xf32>) -> tensor<1x128x8xf32>
  // CHECK-NEXT: return [[RESULT]] : tensor<1x128x8xf32>

func.func @fake_quant_with_min_max_vars(%arg0: tensor<1x1x28x48xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<1x1x28x48xf32> {
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {device = "", narrow_range = true, num_bits = 8 : i64} : (tensor<1x1x28x48xf32>, tensor<f32>, tensor<f32>) -> tensor<1x1x28x48xf32>
  func.return %0 : tensor<1x1x28x48xf32>
  // CHECK-SKIP: tf.FakeQuantWithMinMaxVars
  // CHECK-NOSKIP-NOT: tf.

//   func.return %0#0 : tensor<1x1x2x8xf32>
// }
// COM: CHECK: func.func @main() -> tensor<1x1x2x8xf32>
// COM: CHECK-DAG: %[[CONST:.*]] = stablehlo.constant dense<{{.*}}> : tensor<1x1x2x8xf32>

  return %2 : tensor<1x1x1x4xf32>
}
func.func private @XlaCallModule_aten.avg_pool2d.default.impl_2(%arg0: tensor<1x1x1x8xf32>) -> tensor<1x1x1x4xf32>

// CHECK-LABEL: avg_pool2d_3
// CHECK: %cst = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
// CHECK: %0 = "tfl.transpose"(%arg0, %cst) : (tensor<1x1x1x8xf32>, tensor<4xi32>) -> tensor<1x1x8x1xf32>

  func.return %mm_s : tensor<1x1x12x3xf32>

// CHECK: %[[w:.*]] = arith.constant dense<1.270000e+02> : tensor<1x1x12x512xf32>
// CHECK: %[[mm:.*]] = "tfl.batch_matmul"(%[[w]], %arg0) <{adj_x = false, adj_y = true}>
// CHECK: return %[[mm:.*]]

// PerTensor: %[[w:.*]] = arith.constant dense<1.270000e+02> : tensor<1x1x12x512xf32>

  %cst2 = arith.constant dense<2.0> : tensor<1x1x128xf32>

  %0 = "tfl.fully_connected" (%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1024x1x126xf32>, tensor<128x126xf32>, none) -> (tensor<1024x1x128xf32>)
  %1 = "tfl.add"(%0, %cst2) {fused_activation_function = "NONE"} : (tensor<1024x1x128xf32>, tensor<1x1x128xf32>) -> tensor<1024x1x128xf32>