}
    }
    return success();
  }

  void rewrite(quantfork::DequantizeCastOp op,
               PatternRewriter& rewriter) const final {
    // Rewrite the floating-point ops to the quantized version, by fusing
    // preceding dequantize ops and succeding quantize ops.
    for (Operation* op_with_region : op.getResult().getUsers()) {
      // Collect all the quantized inputs and "clone" the matched op by these
      // inputs.

        quant_params_.quant_spec.nodes_blocklist;
    CustomMap custom_map = quant_params_.quant_spec.custom_map;

    // Rewrite the floating-point ops to the quantized version, by fusing
    // preceding dequantize ops and succeding quantize ops.
    for (Operation* quantizing_op : quantizing_ops) {
      // If it is requantize op, we shouldn't rewrite this op.

            if ( size < 33 || ( 4 + size ) > this.getContext().getConfig().getReceiveBufferSize() ) {
                /* log message? */
                log.warn("Flusing stream input");
                this.in.skip(this.in.available());
            }
            else {
                Response notification = createNotification(key);
                if ( notification != null ) {

// After this optimization:
//   %cst = arith.constant dense<1.0> : tensor<f32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<f32>)
// This pattern can enable more fusing opportunities when the binary op is
// following conv ops.
template <typename BinaryOpType>
struct ScalarizeSplatConstantForBroadcastableOps
    : public OpRewritePattern<BinaryOpType> {

le","stripAndCollapse","getClass","classesToArray","removeProp","propFix","tabindex","for","class","addClass","classNames","curValue","finalValue","removeClass","toggleClass","stateVal","isValidValue","hasClass","rreturn","valHooks","optionSet","focusin","rfocusMorph","stopPropagationCallback","onlyHandlers","bubbleType","ontype","lastElement","eventPath","parentWindow","simulate","triggerHandler","attaches","rquery","parseXML","parserErrorElem","DOMParser","parseFromString","rbracket","rCRLF","...

le","stripAndCollapse","getClass","classesToArray","removeProp","propFix","tabindex","for","class","addClass","classNames","curValue","finalValue","removeClass","toggleClass","stateVal","isValidValue","hasClass","rreturn","valHooks","optionSet","focusin","rfocusMorph","stopPropagationCallback","onlyHandlers","bubbleType","ontype","lastElement","eventPath","parentWindow","simulate","triggerHandler","attaches","rquery","parseXML","parserErrorElem","DOMParser","parseFromString","rbracket","rCRLF","...

        if activation_fn is not None:
          # The accuracy is not good when having FusedBatchNorm without
          # activation in this test.
          if has_batch_norm:
            # Fusing is supported for non-training case.
            out, _, _, _, _, _ = nn_ops.fused_batch_norm_v3(
                out, scale, offset, mean, variance, is_training=False
            )
          out = activation_fn(out)

// hangout : 2014-11-13 Charleston Road Registry Inc.
hangout

// haus : 2013-12-05 Dog Beach, LLC
haus

// hbo : 2015-07-30 HBO Registry Services, Inc.
hbo

// hdfc : 2015-07-30 HOUSING DEVELOPMENT FINANCE CORPORATION LIMITED
hdfc

// hdfcbank : 2015-02-12 HDFC Bank Limited
hdfcbank

// health : 2015-02-11 DotHealth, LLC
health

// healthcare : 2014-06-12 Binky Moon, LLC

inherit;color:inherit;margin:0;padding:0}button,select{text-transform:none}button,input:where([type=button]),input:where([type=reset]),input:where([type=submit]){-webkit-appearance:button;background-color:transparent;background-image:none}:-moz-focusring{outline:auto}:-moz-ui-invalid{box-shadow:none}progress{vertical-align:baseline}::-webkit-inner-spin-button,::-webkit-outer-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}::-webkit-search-decoration{-webkit...

*   `tf.nn.embedding_lookup_sparse`

    *   Optimized this function for some cases by fusing internal operations.

*   `tf.saved_model.SaveOptions`