ir.ORECOVERFP,
		ir.OSTR2BYTES,
		ir.OSTR2BYTESTMP,
		ir.OSTR2RUNES:

		if isRuneCount(n) {
			// len([]rune(s)) is rewritten to runtime.countrunes(s) later.
			conv := n.(*ir.UnaryExpr).X.(*ir.ConvExpr)
			conv.X = o.expr(conv.X, nil)
		} else {
			o.call(n)
		}

		if lhs == nil || lhs.Op() != ir.ONAME || base.Flag.Cfg.Instrumenting {
			return o.copyExpr(n)
		}
		return n

  // FakeQuant).
  bool disable_per_channel = false;

  // Whether to disable per-channel weight quantization and enable legacy per
  // tensor quantization. The legacy quantization for Dense layers is
  // inconsistent with Conv 1x1 which always performs per channel quantization.
  bool disable_per_channel_for_dense_layers = false;

  // Whether to use fixed output ranges of the activation ops (tanh, sigmoid,
  // etc.) and not infer weight constants.

func shouldClose(major, minor int, header Header, removeCloseHeader bool) bool {
	if major < 1 {
		return true
	}

	conv := header["Connection"]
	hasClose := httpguts.HeaderValuesContainsToken(conv, "close")
	if major == 1 && minor == 0 {
		return hasClose || !httpguts.HeaderValuesContainsToken(conv, "keep-alive")
	}

	if hasClose && removeCloseHeader {
		header.Del("Connection")
	}

	return hasClose
}

		}

		// hv2 := rune(ha[hv1])
		nind := ir.NewIndexExpr(base.Pos, ha, hv1)
		nind.SetBounded(true)
		body = append(body, ir.NewAssignStmt(base.Pos, hv2, typecheck.Conv(nind, types.RuneType)))

		// if hv2 < utf8.RuneSelf
		nif := ir.NewIfStmt(base.Pos, nil, nil, nil)
		nif.Cond = ir.NewBinaryExpr(base.Pos, ir.OLT, hv2, ir.NewInt(base.Pos, utf8.RuneSelf))

		// hv1++

    const auto float_graph = model_->subgraphs()->Get(subgraph_idx);
    ASSERT_EQ(quantized_graph->tensors()->size(),
              float_graph->tensors()->size());
    // Make sure the graph only has one Conv operation.
    ASSERT_EQ(quantized_graph->operators()->size(), 1);
    const auto op = quantized_graph->operators()->Get(0);
    const uint32_t op_code_idx = op->opcode_index();

    type = mlir::cast<ShapedType>(cst.getType());
  } else {
    result.can_compress = false;
    return result;
  }

  // Currently we only support compressing weights of ops:
  //   Conv, DepthwiseConv, TransposeConv, whose filter has rank 4, and
  //   FullyConnected, whose filter has rank 2.
  if (type.getRank() != 2 && type.getRank() != 4) {
    result.can_compress = false;
    return result;
  }

	{"slicerunetostring", funcTag, 48},
	{"stringtoslicebyte", funcTag, 50},
	{"stringtoslicerune", funcTag, 53},
	{"slicecopy", funcTag, 54},
	{"decoderune", funcTag, 55},
	{"countrunes", funcTag, 56},
	{"convT", funcTag, 57},
	{"convTnoptr", funcTag, 57},
	{"convT16", funcTag, 59},
	{"convT32", funcTag, 61},
	{"convT64", funcTag, 62},
	{"convTstring", funcTag, 63},
	{"convTslice", funcTag, 66},
	{"assertE2I", funcTag, 67},

            input_tensor,
            self.filters,
            strides=strides,
            dilations=dilations,
            padding=padding,
            data_format='NHWC',
            name='sample/conv',
        )
        if bias_fn is not None:
          out = nn_ops.bias_add(out, self.bias)
        if has_batch_norm:
          # Fusing is supported for non-training case.

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/xla_data.pb.h"

namespace mlir::quant {
namespace {

constexpr StringRef kTfQuantCreatedEinsum = "__tf_quant_created_einsum";

// Replaces mixed-type Conv and Matmul cast hacks with TF XLA ops.
// TODO(b/228403741): Support conversion for dynamic-shaped TF ops.
class ReplaceCastHacksWithTFXLAOpsPass
    : public PassWrapper<ReplaceCastHacksWithTFXLAOpsPass,

                                                Operation* target_op,
                                                int operand_index);

  // Adjusts bias scale that is derived from other scales (fc, conv ops) to
  // prevent overflow of quantized bias values. This also changes quantization
  // state of other inputs when needed.
  bool SetBiasParamsWithAdjustments(Operation* op, int bias_index,