%0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<[1, 3]> : tensor<2xi64>}> : (tensor<2x3xf32>) -> tensor<1x2x2x3xf32>
  %1 = mhlo.multiply %0, %cst1 : tensor<1x2x2x3xf32>
  // CHECK:      return %[[RES]] : tensor<1x2x2x3xf32>
  func.return %1 : tensor<1x2x2x3xf32>
}

// CHECK-LABEL: @foldBroadcastInDimBeforeMulOp_bcast_dim_1D_int

    : (tensor<?x3x2x1xi8>, tensor<2x1x1x1xi8>) -> tensor<?x2x2x1xi32>
  %1 = chlo.broadcast_add %0, %zp_offset : (
      tensor<?x2x2x1xi32>, tensor<?x2x2x1xi32>) -> tensor<?x2x2x1xi32>
  %2 = chlo.broadcast_add %1, %bias : (
      tensor<?x2x2x1xi32>, tensor<1xi32>) ->tensor<?x2x2x1xi32>
  %3 = chlo.broadcast_add %2, %bias : (
      tensor<?x2x2x1xi32>, tensor<1xi32>) ->tensor<?x2x2x1xi32>
  return %3 : tensor<?x2x2x1xi32>
}

// -----

      platforms = [], version = 4 : i64
    } : (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>, tensor<2xf32>) -> tensor<1x2x2x2xf32>
    %2 = "quantfork.stats"(%1) {layerStats = dense<[0.000000e+00, 6.000000e+00]> : tensor<2xf32>} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
    return %2 : tensor<1x2x2x2xf32>
  }

  // CHECK-LABEL: composite_conv2d_with_bias_and_relu6_fn_10

    has_token_input_output = false, module = "", platforms = [],
    version = 5 : i64
  } : (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x2x2x2xf32>
  return %0 : tensor<1x2x2x2xf32>
}

// CHECK-LABEL: func.func @qdq_for_conv_weight_empty
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32>
// CHECK: %[[CST:.+]] = "tf.Const"() <{value = dense<3.000000e-01> : tensor<2x3x3x2xf32>}> : () -> tensor<2x3x3x2xf32>

}

func.func @cast(%arg0: tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> {
  %0 = "tf.Cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
  func.return %0 : tensor<1x2x2x5xf32>

  // CHECK-LABEL: cast
  // CHECK: "tfl.cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
}

func.func @castFloat32ToI16(%arg0: tensor<1x2x2x5xf32>) -> tensor<1x2x2x5xi16> {

// CHECK-SAME: (tensor<1x2x2x3xi8>, tensor<2x2x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>, tensor<f32>, tensor<i32>) -> tensor<*xf32>
// CHECK: return %[[conv_quant]]

// CHECK-LABEL: func private @quantized_conv2d_with_bias_and_relu6_float_output_fn_0

    } : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32>
    // CHECK-SKIP: tf.PartitionedCall
    // CHECK-NOSKIP: call @some_other_func
    // CHECK-NOSKIP-NOT: tf.PartitionedCall
    func.return %1: tensor<1x2x2x3xf32>
  }

  // CHECK-SKIP: func.func private @some_func
  func.func private @some_func(%arg0: tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32> attributes {tf._noinline = true} {

    }> {
      _collective_manager_ids = [], device = ""
    } : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32>
    func.return %0: tensor<1x2x2x3xf32>
  }

  func.func private @some_func(%arg0: tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32> {
    return %arg0 : tensor<1x2x2x3xf32>
  }
}

// CHECK: module
// CHECK-NOT: tf.PartitionedCall
// CHECK-NOT: some_func

// -----

module {
  func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
    %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    func.return %1: tensor<*xf32>
  }

  %3 = "tfl.quantize"(%2) {qtype = tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>, volatile} : (tensor<1x2x1x3xf32>) -> tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>
  %5 = "tfl.dequantize"(%arg0) : (tensor<1x2x1x3x!quant.uniform<i8:f64, 1.0>>) -> tensor<1x2x1x3xf32>
  "tfl.assign_variable"(%1, %5) : (tensor<!tf_type.resource>, tensor<1x2x1x3xf32>) -> ()