attrs=[
        'stride_w: int', 'stride_h: int', 'dilation_w: int', 'dilation_h: int',
        'padding: {"SAME", "VALID"}', 'act: {"", "RELU", "RELU6", "TANH"} = ""'
    ],
    derived_attrs=['T: {float, int8}'],
    outputs=['o: T'])
def _composite_conv_add_relu(input_, filter_, bias, stride_w, stride_h,
                             dilation_w, dilation_h, padding, act):
  res = tf.raw_ops.Conv2D(
      input=input_,

    bias = tf.zeros([8])
    kwargs = {
        'input_': input_,
        'filter_': filter_,
        'bias': bias,
        'stride_w': 2,
        'stride_h': 2,
        'dilation_w': 1,
        'dilation_h': 1,
        'padding': 'SAME',
        'act': 'RELU'
    }

    self._assertOpAndComposite([input_, filter_, bias],
                               tf.function(gen_mnist_ops.new_conv2d),

      (NamedAttr<"explicit_paddings"> $explicit_paddings),
      (NamedAttr<"dilations"> $dilations))),
  [(IsNotInLiftedFunc $res), (IsConstTensor $filter)], [], (addBenefit 1)>;

def LiftDepthwiseConv : Pat<
  (TF_DepthwiseConv2dNativeOp:$res $input, $filter, $strides, $padding,
    $explicit_paddings, IsDataFormatNHWC:$data_format, $dilations),
  (LiftAsTFPartitionedCall<"composite_depthwise_conv2d_fn">

    %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {
      attr_map = "0:strides,1:padding,2:explicit_paddings,3:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1]
    } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

    %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {
      attr_map = "0:strides,1:padding,2:explicit_paddings,3:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1]
    } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

// RUN: tf-opt %s -tf-layout-assignment=force-data-format=NCHW -verify-diagnostics | FileCheck %s --dump-input=always

// IMPORTANT: In the following Conv2D tests tensor shapes do not match
// convolution parameters (stride, dilations, etc...). This test only verifies
// that changing convolution data layout will update all the attributes.

// CHECK-LABEL: func @transposeConv2D

  %3 = "tf.AddV2"(%2, %1): (tensor<15x28x28x1xf32>, tensor<1xf32>) -> tensor<15x28x28x1xf32>
  func.return %2 : tensor<15x28x28x1xf32>

    %0 = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x512xf32>, tensor<2x3x512x2xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

  %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xbf16>
  %1 = "tf.Conv2D"(%0, %cst) {data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1]}
       : (tensor<1x3x4x3xbf16>, tensor<2x3x3x2xbf16>) -> tensor<1x3x2x2xbf16> loc("Model/conv2d")

// CHECK-LABEL: conv_2d_bf16
func.func @conv_2d_bf16(%arg0 : tensor<256x32x32x3xbf16>, %arg1 : tensor<3x3x3x16xbf16>) -> tensor<256x8x7x16xbf16> {
  %0 = "tf.Conv2D"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xbf16>, tensor<3x3x3x16xbf16>) -> tensor<256x8x7x16xbf16>
  func.return %0 : tensor<256x8x7x16xbf16>
  // CHECK: "tfl.conv_2d"
}