// Apply float16 quantization to all the weights. Quantized weights will be
    // dequantized before running inference.
    // Activation: f32, Weight: f16, Bias: f16
    FLOAT16 = 3;

    // Apply static range quantization. The quantization range is determined
    // via calibration phase and quantized during conversion.
    // Activation: qi8, Weight: qi8, Bias: qi32
    POST_TRAINING_QUANTIZATION_STATIC_RANGE = 4;
  }

}

// NamedEdgeMap contains all unique call edges in the profile and their
// edge weight.
type NamedEdgeMap struct {
	Weight map[NamedCallEdge]int64

	// ByWeight lists all keys in Weight, sorted by edge weight from
	// highest to lowest.
	ByWeight []NamedCallEdge
}

func emptyProfile() *Profile {
	// Initialize empty maps/slices for easier use without a requiring a

    # output shape: [-1, 1024]
    fc1 = gen_mnist_ops.new_fully_connected(reshape, self.weights['f3'],
                                            self.biases['b3'], 'RELU')
    # output shape: [-1, 10]
    return gen_mnist_ops.new_fully_connected(fc1, self.weights['f4'],
                                             self.biases['b4'])


def main(strategy):

   (HasStaticShapeConstraint $weight)],
  [], (addBenefit 10)>;

// Convert Matmul with hybrid inputs (f32 activation/int8 weight) to XlaDotV2
def ConvertTFMatMulToXLADotV2OpWeightOnly : Pat<
  (TF_MatMulOp:$matmul
    $input,
    (TF_MulOp (TF_CastOp (TF_IdentityOp $weight), $truncate1), $scale),
    $transpose_a, $transpose_b, $grad_a, $grad_b),
  (TF_MulOp (CreateXlaDotV2OpFromTfMatMulOp

      }
    }
  }
}
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: "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 {

	g, err := community.NewUndirectedLayers(friends, enemies)
	if err != nil {
		log.Fatal(err)
	}
	weights := []float64{1, -1}

	// Get the profile of internal node weight for resolutions
	// between 0.1 and 10 using logarithmic bisection.
	p, err := community.Profile(
		community.ModularMultiplexScore(g, weights, true, community.WeightMultiplex, 10, src),
		true, 1e-3, 0.1, 10,
	)
	if err != nil {
		log.Fatal(err)

	// 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) {

      }
    }
  }
}
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"

  }];
  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",