}
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights"
      }
    }
  }
}
node {

	// by providing weights in LocalityLbEndpoints via load_balancing_weight.
	// By setting weights across different localities, it can allow
	// Envoy to do weighted load balancing across different zones and geographical locations.
	for _, localityWeightSetting := range distribute {
		if localityWeightSetting != nil &&
			util.LocalityMatch(locality, localityWeightSetting.From) {

  }];
  let dependentDialects = ["mlir::stablehlo::StablehloDialect"];
}

def InsertWeightParamPass : Pass<"stablehlo-insert-weight-param", "mlir::func::FuncOp"> {
  let summary = "Insert quantization parameters of weights for weight-only quantization and dynamic range quantization.";
  let dependentDialects = [
      "mlir::stablehlo::StablehloDialect",
      "TF::TensorFlowDialect",

      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights"
      }
    }
  }
}
node {

  let description = [{
      This pass encodes sparse weights in the model in the proper format, and adds
      Densify() op if necessary. The general algorithm is:
        1. Get list of operands (weights) of an op that can be sparse.
        2. Get list of supported block configurations of the op.
        3. Calculate random sparsity of the weight.
          3.1. If sparsity level is below the encoding threshold, keep in dense.

      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights"
      }
    }
  }
}
node {

      }
    }
  }
}
node {
  name: "MobilenetV1/Conv2d_0/weights/read"
  op: "Identity"
  input: "MobilenetV1/Conv2d_0/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@MobilenetV1/Conv2d_0/weights"
      }
    }
  }
}
node {
  name: "MobilenetV1/MobilenetV1/Conv2d_0/Conv2D"

  const bool disable_per_channel_;

  // We should distinguish weights and bias constants. Biases are specified by
  // the quantization spec or are the operands of ops with same scale spec. The
  // rest are weights.
  DenseSet<Operation*> weights_;

  // The weights require narrow_range quantization. This map collects all the
  // weight operands defined by the op quant spec. The value of each entry is

namespace {

using ::stablehlo::quantization::Method;
using ::stablehlo::quantization::QuantizedType;
using ::stablehlo::quantization::WeightOnlyPtq;

// Inserts quantization parameters of weights for weight-only quantization and
// dynamic range quantization of `stablehlo.convolution` and
// `stablehlo.dot_general`.
class InsertWeightParamPass
    : public impl::InsertWeightParamPassBase<InsertWeightParamPass> {
 public:

      }
    }
  }
}
node {
  name: "MobilenetV1/Conv2d_0/weights/read"
  op: "Identity"
  input: "MobilenetV1/Conv2d_0/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@MobilenetV1/Conv2d_0/weights"
      }
    }
  }
}
node {
  name: "MobilenetV1/MobilenetV1/Conv2d_0/Conv2D"