// CHECK: }

// CHECK-LABEL: private @composite_conv_with_bias_dynamic_fn_1
// CHECK: %[[CONV:.*]] = stablehlo.convolution(%arg0, %arg1)
// CHECK: %[[SHAPE_OF:.*]] = shape.shape_of %[[CONV]]
// CHECK: %[[DYNAMIC_BROADCAST_IN_DIM:.*]] = stablehlo.dynamic_broadcast_in_dim %arg2, %[[SHAPE_OF]]
// CHECK: %[[ADD:.*]] = stablehlo.add %[[CONV]], %[[DYNAMIC_BROADCAST_IN_DIM]]
// CHECK: return %[[ADD]] : tensor<?x28x28x16xf32>
// CHECK: }

  %conv = "tfl.conv_2d"(%input, %weight, %bias) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32,
    fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32}
  : (tensor<1x224x224x3xf32>, tensor<32x3x3x3xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>
  func.return %conv : tensor<1x112x112x32xf32>

    follow-redirects "^1.0.0"
    requires-port "^1.0.0"

iconv-lite@0.4.24:
  version "0.4.24"
  resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.4.24.tgz#2022b4b25fbddc21d2f524974a474aafe733908b"
  integrity sha512-v3MXnZAcvnywkTUEZomIActle7RXXeedOR31wwl7VlyoXO4Qi9arvSenNQWne1TcRwhCL1HwLI21bEqdpj8/rA==
  dependencies:
    safer-buffer ">= 2.1.2 < 3"

iconv-lite@^0.6.3:
  version "0.6.3"

        Convention conv = new ExtensibleDynamicObject(this, DynamicObjectAwareBean.class, TestUtil.instantiatorFactory().decorateLenient()).getConvention();

        public Convention getConvention() {
            return conv;
        }

        public ExtensionContainer getExtensions() {
            return conv;
        }

        # One pure conv
        out = nn_ops.conv2d(
            out,
            self.conv_filters,
            strides=(1, 1, 2, 1),
            dilations=(1, 1, 1, 1),
            padding='SAME',
            data_format='NHWC',
        )

        # One fakequant attached conv
        if is_qat_model:
          out = array_ops.fake_quant_with_min_max_args(

	}

	closeReturned := make(chan bool, 1)
	go func() {
		<-inWrite
		tconn.Close() // test that this doesn't block forever.
		closeReturned <- true
	}()

	_, err = tconn.Write([]byte("foo"))
	if err != errConnClosed {
		t.Errorf("Write error = %v; want errConnClosed", err)
	}

	<-closeReturned
	if err := tconn.Close(); err != net.ErrClosed {
		t.Errorf("Close error = %v; want net.ErrClosed", err)
	}

			conv.getTypeIDs[n.Go[:len(n.Go)-9]] = true
		}
	}
	for i, n := range names {
		if types[i] == nil {
			continue
		}
		pos := f.NamePos[n]
		f, fok := types[i].(*dwarf.FuncType)
		if n.Kind != "type" && fok {
			n.Kind = "func"
			n.FuncType = conv.FuncType(f, pos)
		} else {
			n.Type = conv.Type(types[i], pos)
			switch n.Kind {
			case "iconst":

// CHECK: %[[PAD:.*]] = "tf.PadV2"({{.*}}, %[[CONST]], %[[CONST_1]])
// CHECK: %[[CONV:.*]] = "tf.XlaConvV2"(%[[PAD]], %[[WEIGHT]]
// CHECK-SAME: (tensor<1x4x5x5x3xi8>, tensor<2x3x3x3x2xi8>, tensor<3xi32>, tensor<3x2xi32>, tensor<3xi32>, tensor<3xi32>, tensor<i32>) -> tensor<1x3x2x3x2xi32>
// CHECK: %[[SUB:.*]] = "tf.Sub"(%[[CONV]], %[[CONST_2]])
}

// -----

module attributes {tf_saved_model.semantics} {

      auto pad_output_type = UnrankedTensorType::get(elem_type);
      input = rewriter.create<TF::PadOp>(op->getLoc(), pad_output_type, input,
                                         padding_const);

      // Set Conv padding to `VALID` since padding has been handled by Pad op.
      state.padding = rewriter.getStringAttr("VALID");
    }
    auto conv_op = static_cast<const ConcreteType *>(this)->createTFLOp(

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {

func.func @conv(tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>, tensor<256x3x32x32xf32>) -> (tensor<256x8x7x16xf32>, tensor<256x16x32x32xf32>, tensor<256x8x6x16xf32>, tensor<256x32x32x16xf32>, tensor<256x32x32x16xf32>) {