return %1 : tensor<1x3xf32>
  }

      // Represents a per-channel quantization for the operand index 1 with
      // quantization dimension of 3
      _quantization_method = "static_range_ptq {input_quantized_types {key: 1, value {dimension_specs {dimension: 3}}}}",
      platforms = [], version = 4 : i64
    } : (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>, tensor<2xf32>) -> tensor<1x2x2x2xf32>

// weights, on the channel dimension.
//
// In textproto, the spec corresponds to:
//
// {
//   {matcher {function_name {regex: "composite_conv.*"}}}
//   {method {static_range_ptq
//     {input_quantized_types {
//       key: 1,
//       value {dimension_specs {dimension: 3}}}}
//   }}
// }
QuantizationSpec GetPtqSpecForConvolution(Method::MethodCase method_case) {
  QuantizationSpec spec{};

    // Look for quantized dimension specs for each quantized type and
    // populate `coeff_op_quant_dim`.
    for (const auto& [operand_idx, quantized_type] :
         static_range_ptq_spec.input_quantized_types()) {
      if (quantized_type.has_dimension_specs()) {
        spec.coeff_op_quant_dim[operand_idx] =
            quantized_type.dimension_specs().dimension();
      }
    }
  }
}

}  // namespace