// CHECK-DAG: %[[CONST_0:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2x3x3x2xf32>
// CHECK-DAG: %[[CONV:.*]] = stablehlo.convolution(%[[ARG]], %[[CONST_1]]) {{.*}} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
// CHECK-DAG: %[[BROADCAST:.*]] = stablehlo.broadcast_in_dim %[[CONST_0]], dims = [3] : (tensor<2xf32>) -> tensor<1x3x2x2xf32>

  // once available.
  //
  // If set to true, enable channel-wise quantization for:
  //   * Convolution ops: When the attached `Method` also specifies per-channel
  //                      quantization.
  //   * Non-convolution ops: All
  //
  // Default value: true
  bool enable_per_channel_quantized_weight = 2 [deprecated = true];

    %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }
// Checks that the entry function is quantized for convolution. Quantized

      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@conv_net_2d/conv_2d_0/w"
      }
    }
  }
}
node {
  name: "conv_net_2d_1/conv_2d_0/convolution"
  op: "Conv2D"
  input: "input"
  input: "conv_net_2d/conv_2d_0/w/read"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "data_format"
    value {

    : public PassWrapper<FuseMhloConvolutionPass, OperationPass<func::FuncOp>> {
 public:
  StringRef getArgument() const final { return "fuse-mhlo-convolution-pass"; }
  StringRef getDescription() const final {
    return "Fuses MHLO binary element-wise ops and convolution op";
  }

  void runOnOperation() override {
    RewritePatternSet patterns(&getContext());

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

// IMPORTANT: 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
func.func @transposeConv2D(%input: tensor<1x3x32x32xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32> {

  return %0 : tensor<1x3x3x2x!quant.uniform<i8:f32, 4.000000e+0>>
}
// Confirm that the `stablehlo.convolution` is not converted to `tfl.conv_2d`.
// CHECK-LABEL: convolution_upstream_srq_non_const_filter
// CHECK-SAME: %[[ARG:.+]]: tensor<1x3x3x4x!quant.uniform<i8:f32, 1.000000e+00:-100>>
// CHECK: stablehlo.convolution
// CHECK-NOT: tfl.conv_2d

// -----

// CHECK: @uniform_dequantize_0
}

// -----

// Tests a variant where there is no stablehlo.convert op in between the
// filter constant and the convolution op.
//
// `filter (f32) -> convolution`
//
// instead of:
//
// `filter (i8) -> convert (i8 -> f32) -> convolution`

module {
// CHECK-LABEL: quantized_conv_op_with_no_filter_convert
// CHECK-SAME: %[[ARG:.*]]: tensor<1x3x3x4xf32>

            .getDimSize(input_feature_dimension);

    // Check for Group Convolution parameters
    if (feature_group_count != 1 && feature_group_count != input_channels) {
      // Group convolution is not supported yet.
      return rewriter.notifyMatchFailure(conv_op,
                                         "doesn't support group convolution");
    }

    auto input_spatial_dimensions = dnums.getInputSpatialDimensions();

        %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>
        %1 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32>