github.com/prometheus/prometheus v0.52.1/go.mod h1:3z74cVsmVH0iXOR5QBjB7Pa6A0KJeEAK5A6UsmAFb1g=
github.com/prometheus/tsdb v0.7.1/go.mod h1:qhTCs0VvXwvX/y3TZrWD7rabWM+ijKTux40TwIPHuXU=
github.com/quic-go/qpack v0.4.0 h1:Cr9BXA1sQS2SmDUWjSofMPNKmvF6IiIfDRmgU0w1ZCo=
github.com/quic-go/qpack v0.4.0/go.mod h1:UZVnYIfi5GRk+zI9UMaCPsmZ2xKJP7XBUvVyT1Knj9A=
github.com/quic-go/quic-go v0.44.0 h1:So5wOr7jyO4vzL2sd8/pD9Kesciv91zSk8BoFngItQ0=

  const bool fully_quantize = !allow_float;

  flatbuffers::FlatBufferBuilder input_builder;
  tflite::FinishModelBuffer(input_builder,
                            tflite::Model::Pack(input_builder, model));

  const std::string input_buffer(
      reinterpret_cast<const char*>(input_builder.GetBufferPointer()),
      input_builder.GetSize());
  auto status = mlir::lite::QuantizeModel(

		x = -x
	}
	return x, nil
}

// The "std" value passed to appendNano contains two packed fields: the number of
// digits after the decimal and the separator character (period or comma).
// These functions pack and unpack that variable.
func stdFracSecond(code, n, c int) int {
	// Use 0xfff to make the failure case even more absurd.
	if c == '.' {
		return code | ((n & 0xfff) << stdArgShift)
	}

  }];
}



def UnrollBatchMatMulPass : Pass<"tf-unroll-batch-matmul", "mlir::func::FuncOp"> {
  let summary = "Unroll TF BatchMatMul op into Reshape, Slice, MatMul, Pack ops.";
  let constructor = "TF::CreateUnrollBatchMatMulPassPass()";
}

def ClusterFormationPass : Pass<"tf-device-cluster-formation", "mlir::ModuleOp"> {

if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
Etc.

For example:

```
# 'x' is [1, 4]
# 'y' is [2, 5]
# 'z' is [3, 6]
pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
```

This is the opposite of `unpack`.
  }];

  let arguments = (ins

using ComputedQueryFn = function_ref<bool(ValuePort)>;
using ValueQueryFn = function_ref<Attribute(const ValuePort&)>;
using ValuePortInputs = SmallVectorImpl<ValuePort>;

// Note: Following implements the rank 1 pack op case so could be
// generalized.
//
// Maps the specified component in the `port` of the given op's result to one of
// the element in the input.

`tf.reverse_v2()` will remain until 1.0 final. * `tf.mul`, `tf.sub` and `tf.neg`
are deprecated in favor of `tf.multiply`, `tf.subtract` and `tf.negative`. *
`tf.pack` and `tf.unpack` are deprecated in favor of `tf.stack` and
`tf.unstack`. * `TensorArray.pack` and `TensorArray.unpack` are getting
deprecated in favor of `TensorArray.stack` and `TensorArray.unstack`. * The