(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) {

  }];
  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",

from fastapi import FastAPI

app = FastAPI()


@app.get("/keyword-weights/", response_model=dict[str, float])
async def read_keyword_weights():

	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)

      }
    }
  }
}
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.