def test_post_body(client: TestClient):
    data = {"2": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


def test_post_invalid_body(client: TestClient):
    data = {"foo": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 422, response.text
    assert response.json() == IsDict(

    data = {"2": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


@needs_py39
def test_post_invalid_body(client: TestClient):
    data = {"foo": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 422, response.text

      }
    }
  }
}
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 {

	// 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 {

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: