// CHECK: %cst_1 = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>
// CHECK: %3 = "tfl.transpose"(%2, %cst_1) : (tensor<1x1x4x1xf32>, tensor<4xi32>) -> tensor<1x1x1x4xf32>
// CHECK: return %3 : tensor<1x1x1x4xf32>

func.func @avg_pool2d_4(%arg0: tensor<1x1x1x9xf32>) -> (tensor<1x1x1x4xf32>) {

}
// CHECK: @main(%[[ARG:.+]]: tensor<1x8x4x4xf32>) -> tensor<1x8x4x4xf32>

// Contains the `stablehlo.transpose` op of the arg (e.g. [b, f, 0, 1] to
// [b, 0, 1, f]). The weight constant is folded into [0, 1, i, o] format.
// CHECK-DAG: %[[CST:.+]] = stablehlo.constant dense<3.000000e+00> : tensor<3x3x8x8xf32>
// CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %arg0, dims = [0, 2, 3, 1] : (tensor<1x8x4x4xf32>) -> tensor<1x4x4x8xf32>

    return %1 : tensor<1x3x4x2xf32>
  }

  func.func private @composite_conv_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32> attributes {_from_xla_call_module} {

        _stablehlo_module_attrs = {}, _tfl_quant_trait = "fully_quantizable",
        device = ""
      } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }

  // CHECK: func.func private @qdq_for_conv_weight_per_channel_default(%[[ARG0:.+]]: tensor<1x3x4x3xf32>)
  // CHECK: %[[CST:.+]] = "tf.Const"() <{value = dense<3.000000e-01> : tensor<2x3x3x2xf32>}> : () -> tensor<2x3x3x2xf32>

        version = 5 : i64
      } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>, tensor<2xf32>) -> tensor<1x3x4x2xf32>
    %2 = "quantfork.stats"(%1) {layerStats = dense<[5.00000000e-6, 7.00000000e-1]> : tensor<2xf32>} : (tensor<1x3x4x2xf32>) -> tensor<1x3x4x2xf32>
    return %2 : tensor<1x3x4x2xf32>
  }
// CHECK: func.func private @quantize_conv_with_bias_1d_fn(%[[ARG_0:.+]]: tensor<1x3x4x3xf32>) -> tensor<1x3x4x2xf32> attributes {tf._original_func_name = "main_0"}

    %14 = stablehlo.subtract %9, %13 : tensor<1x3x3x4xf32>
    %15 = stablehlo.broadcast_in_dim %4, dims = [0, 1, 2, 3] : (tensor<1x1x1x4xf32>) -> tensor<1x3x3x4xf32>
    %16 = stablehlo.multiply %14, %15 : tensor<1x3x3x4xf32>

// Tests that a float `stablehlo.constant` is not converted into `tfl.qconst`.

// CHECK-LABEL: func @float_constant
func.func @float_constant() -> tensor<1x2x4x5xf32> {
  %0 = stablehlo.constant() {value = dense<1.0> : tensor<1x2x4x5xf32>} : () -> tensor<1x2x4x5xf32>
  return %0 : tensor<1x2x4x5xf32>
}

// CHECK: stablehlo.constant
// CHECK-NOT: tfl.pseudo_qconst
// CHECK-NOT: tfl.pseudo_const
// CHECK-NOT: arith.constant

  %result = "tf_device.launch"() ({
    %3 = "tf.Transpose"(%2, %1) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x8xf32>
    tf_device.return %3: tensor<1x4x4x8xf32>
  }) {device = "device"} : () -> tensor<1x4x4x8xf32>

  func.return %result : tensor<1x4x4x8xf32>

  // CHECK-DAG: %[[CONST1:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}>

  func.return %0, %1, %2 : tensor<1x4x4xf32>, tensor<1x4x4xf32>, tensor<14x4x4xf32>
}

// -----

func.func @testSplitVOpWithBadSizeSplitsConstantSum(%arg0: tensor<16x4x4xf32>) -> (tensor<0x4x4xf32>, tensor<16x4x4xf32>) {

  %4 = stablehlo.broadcast_in_dim %1, dims = [3] : (tensor<4xf32>) -> tensor<1x3x3x4xf32>
  %5 = stablehlo.add %3, %4 : tensor<1x3x3x4xf32>
  %6 = stablehlo.maximum %5, %2 : tensor<1x3x3x4xf32>
  func.return %6: tensor<1x3x3x4xf32>
}
// CHECK: %[[CONST_0:.*]] = stablehlo.constant dense<2.000000e+00>