weight_new_shape, &rewriter);

  // Replace the fc with conv.
  // The output would be [1, 1, width, output].
  auto conv_output_type = RankedTensorType::get({1, 1, width, output_size},
                                                output_type.getElementType());
  auto conv = rewriter.create<TFL::Conv2DOp>(
      fc_op.getLoc(), conv_output_type, reshaped_input, reshaped_weight,

// point constant.
def : Pat<(TFL_DequantizeOp
             (TFL_QuantizeOp (Arith_ConstantOp F32ElementsAttr:$cst), $qt)),
          (TFL_ConstOp $cst)>;

// Transpose conv supports hybrid computation with quantized weights.
def FoldQuantWeightsIntoTposeConv : Pat<
  (TFL_TransposeConvOp
    $output_shape,
    (TFL_DequantizeOp $quant_weights),
    $quant_input,

      mlir::mhlo::createLegalizeDotToDotGeneralPass());
  // Unfuse mhlo BatchNorm to primitive ops.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createUnfuseBatchNormPass());
  // Fuse Conv + Mul to Conv.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFuseConvolutionPass());
  // Fold broadcast_in_dim + Mul.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFoldBroadcastPass());

	src = typecheck.Expr(src)
	if dst.Kind() == types.TBLANK || types.Identical(dst, src.Type()) {
		return src
	}

	n := ir.NewConvExpr(nrange.Pos(), ir.OCONV, dst, src)
	n.TypeWord = typeWord
	n.SrcRType = srcRType
	return typecheck.Expr(n)
}

// isMapClear checks if n is of the form:
//
//	for k := range m {
//		delete(m, k)
//	}
//

// has a zero MTIME. This is a requirement for the Debian maintainers
// to be able to have deterministic packages.
//
// To patch a .gz file, use the following command:
//
//	$ dd if=/dev/zero bs=1 seek=4 count=4 conv=notrunc of=filename.gz
//
// See https://golang.org/issue/14937.
func TestGZIPFilesHaveZeroMTimes(t *testing.T) {
	// To avoid spurious false positives due to untracked GZIP files that

			got := tt.enc.EncodeToString([]byte(p.decoded))
			testEqual(t, "Encode(%q) = %q, want %q", p.decoded, got, tt.conv(p.encoded))
			dst := tt.enc.AppendEncode([]byte("lead"), []byte(p.decoded))
			testEqual(t, `AppendEncode("lead", %q) = %q, want %q`, p.decoded, string(dst), "lead"+tt.conv(p.encoded))
		}
	}
}

func TestEncoder(t *testing.T) {
	for _, p := range pairs {
		bb := &strings.Builder{}

         (tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) ->
         tensor<64x3x3x3xf32>
    %conv = "tfl.conv_2d"(%input_act, %w, %bias)

    but if it is supported, it will be rewritten as:

    %q_w = "tfl.pseudo_qconst"() {
         qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
    %conv = "tfl.conv_2d"(%input_act, %q_w, %bias)

  // CHECK-SAME: [1.300000e+01, 2.800000e+01], [1.500000e+01, 3.200000e+01], [1.700000e+01, 3.600000e+01]
  // CHECK: %[[CONV:.*]] = "tf.Conv2D"(%arg0, %[[CST]]) <{data_format = "NHWC", dilations = [1, 2, 3, 1], explicit_paddings = [], padding = "SAME", strides = [1, 4, 5, 1], use_cudnn_on_gpu = true}>
  // CHECK: return %[[CONV]] : tensor<1x28x23x2xf32>
}

// CHECK-LABEL: @notfuseMulIntoConv2d
// filter and multiply are not broadcastable

	}
	return addr.Offset
}

// getRegister checks that addr represents a register and returns its value.
func (p *Parser) getRegister(prog *obj.Prog, op obj.As, addr *obj.Addr) int16 {
	if addr.Type != obj.TYPE_REG || addr.Offset != 0 || addr.Name != 0 || addr.Index != 0 {
		p.errorf("%s: expected register; found %s", op, obj.Dconv(prog, addr))
	}
	return addr.Reg

  ASSERT_EQ(status.message(),
            "'optimize_func' field in TP_Optimizer must be set.");
}

TEST(TF_GrapplerItem, NodesToPreserve) {
  GrapplerItem item;
  item.fetch = std::vector<string>{"Conv", "BiasAdd"};
  std::unordered_set<string> nodes_preserved = item.NodesToPreserve();
  TF_GrapplerItem* c_item = reinterpret_cast<TF_GrapplerItem*>(&item);

  int list_total_size = 0;