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,

			return true
		}
		// We may have skipped past one or more OCONVNOPs, so
		// use conv to ensure r is assignable to l (#13263).
		dst := ir.Node(l)
		if loff != 0 || !types.Identical(typ, l.Type()) {
			dst = ir.NewNameOffsetExpr(base.Pos, l, loff, typ)
		}
		s.append(ir.NewAssignStmt(base.Pos, dst, typecheck.Conv(r, typ)))
		return true

	case ir.ONIL:
		return true

	case ir.OLITERAL:

    Option<"is_signed_", "is-signed", "bool", "false",
               "Is the corresponding integer signed">,
  ];
}

def IdentifyDilatedConvPass : Pass<"tfl-identify-dilated-conv", "mlir::func::FuncOp"> {
  let summary = "Convert dense tensor to sparse format.";
  let constructor = "CreateIdentifyDilatedConvPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}