//   ...${key2}...
// }
// ```
// The above template with generate two functions by substituting `key1` and
// `key2` with given values.

module {

  for main_op in ["Conv2D", "DepthwiseConv2D", "MatMul"] {
    parameters[
      {"quantized_ops": ["${main_op}", "BiasAdd"], "act_func": "internal_requantize_no_activation_fn", "output_type": "!tf_type.qint8"},

    if (!function_name.starts_with("quantized_") &&
        !function_name.starts_with("composite_")) {
      return failure();
    }

    if (function_name.contains("depthwise_conv2d")) {
      return addReshapeOpToDepthwiseWeight(call_op, rewriter);
    }

    return failure();
  }
};

// Prints a summary about the quantization results.
class QuantizationSummary {

}

inline const char * const *EnumNamesBuiltinOperator() {
  static const char * const names[208] = {
    "ADD",
    "AVERAGE_POOL_2D",
    "CONCATENATION",
    "CONV_2D",
    "DEPTHWISE_CONV_2D",
    "DEPTH_TO_SPACE",
    "DEQUANTIZE",
    "EMBEDDING_LOOKUP",
    "FLOOR",
    "FULLY_CONNECTED",
    "HASHTABLE_LOOKUP",
    "L2_NORMALIZATION",
    "L2_POOL_2D",

  // Returns the dimension length of the channel dimension and also the slide
  // size by each position in the channel dimension accordingly. tfl.conv2d and
  // tfl.fully_connected has heading channel dimension, but tfl.depthwise_conv2d
  // has tailing channel dimension. This function is to provide a utility to
  // create the above information from the op property.
  static std::pair<int64_t, int64_t> GetBiasDimAndSliceSize(

        collisions across summary types.

*   When running on GPU (with cuDNN version 7.6.3 or
    later),`tf.nn.depthwise_conv2d` backprop to `filter` (and therefore also
    `tf.keras.layers.DepthwiseConv2D`) now operate deterministically (and
    `tf.errors.UnimplementedError` is no longer thrown) when op-determinism has

and a filter / kernel tensor of shape
`[filter_height, filter_width, in_channels, channel_multiplier]`, containing
`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
a different filter to each input channel (expanding from 1 channel to
`channel_multiplier` channels for each), then concatenates the results