scale, offset = [1.0] * 2, [0.5] * 2
        mean, variance = scale, offset
        out = nn_ops.conv2d(
            q_input,
            q_filter,
            strides=[1, 1, 2, 1],
            dilations=[1, 1, 1, 1],
            padding='SAME',
            data_format='NHWC',
            name='sample/conv2d',
        )
        if has_bias:
          out = nn_ops.bias_add(out, bias, data_format='NHWC')

","statusText","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallba...

","statusText","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallba...

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG0]], %[[DQ]])
// CHECK{LITERAL}: dim_numbers = [b, 0, 1, f]x[o, 0, 1, i]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64}
// CHECK-SAME: (tensor<1x3x3x4xf32>, tensor<2x3x3x4xf32>) -> tensor<1x3x3x2xf32>
// CHECK: return %[[CONV]]

// -----

    func.return %arg0 : tensor<*xi32>
  }

  // Test conv2d inferReturnTypes can infer some information when input or
  // filter does not have fully static shape.

  // CHECK-LABEL: func @conv2d_unranked_input_and_filter
  func.func @conv2d_unranked_input_and_filter(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: -> tensor<?x?x?x?xf32>

  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x30x30x16xf32>
  func.return %0 : tensor<256x30x30x16xf32>
}

// -----

func.func @testConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x30x30x16xf32> {
  // expected-error @+1 {{requires positive strides}}

  %con12 = "tf.Const"() { value = dense<[3.0, 4.0]> : tensor<2xf32> } : () -> tensor<2xf32>
  %con21 = "tf.Const"() { value = dense<[0.0, 2.0]> : tensor<2xf32> } : () -> tensor<2xf32>
  %con22 = "tf.Const"() { value = dense<[0.0, 0.0]> : tensor<2xf32> } : () -> tensor<2xf32>
  %con31 = "tf.Const"() { value = dense<[0.0, 0.0]> : tensor<2xf32> } : () -> tensor<2xf32>

  // can let binary op to broadcast elements.
  if (elements_depth == 1) {
    return true;
  }

  // In TFLite Conv2D uses OHWI format for filter, and 1HWO for Depthwise Conv.
  // For conv:
  // Check if last dimension in filter equals the first dimension
  // For depthwise conv:
  // Check if the first in filter dimension equals the first dimension.
  if (filter_shape.empty() ||

      }
      func @_func(%input: tensor<2x112x112x12xf32>, %filter: tensor<7x7x3x64xf32>) {
        %filter_transform = "tf.Pad/tf.Transpose/tf.Reshape"(%filter): tensor<7x7x3x64xf32>) -> tensor<4x4x12x64xf32>
        %conv = "tf.Conv2D"(%input, %filter_transfrom) {strides = [1, 1, 1, 1]}: (tensor<2x112x112x12xf32>, tensor<4x4x12x64xf32>) -> tensor<2x112x112x64xf32>
      }
    }
    ```

            (i != out_batch_dim && out_type.isDynamicDim(i))) {
          return false;
        }
      }
    }

    // All ones in "lhs_dilation" means this "mhlo.conv" op should be
    // converted to "tf.Conv2D" or "tf.DepthwiseConv2dNativeOp".
    auto lhs_dilation = conv_op.getLhsDilation().value();
    if (!lhs_dilation.isSplat() || lhs_dilation.getSplatValue<int64_t>() != 1)
      return false;