// Canonicalization includes const folding, which is utilized here to optimize
  // away ops that can't get constant folded after PrepareTF pass. For example,
  // tf.Conv2D is split into tf.Transpose and tfl.Conv2D.
  pass_manager->addNestedPass<mlir::func::FuncOp>(
      mlir::createCanonicalizerPass());
  pass_manager->addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());

    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();

};

using ConvertConv2DDynamic =
    ConvertConvDynamic<TF::Conv2DOp, /*num_spatial_dims=*/2>;

// Converts the TensorFlow conv op in template to the generic HLO conv op by
// converting TensorFlow op attributes to HLO op attributes.
//
// Sample result for Conv2D:
//
//   %conv = "mhlo.convolution"(%input, %filter) {
//     strides = [1, 2],
//     paddings = [[1, 0], [1, 1]],
//     ...
//   }
//

			withMounts(map[string]string{"/home/r2lib1": "/usr/r2lib1"}),
			withEnvs(map[string]string{"r2devices": "dev1 dev2"}),
		),
	)
	testManager.podDevices.insert("pod1", "con2", resourceName1,
		constructDevices([]string{"dev3"}),
		newContainerAllocateResponse(
			withDevices(map[string]string{"/dev/r1dev3": "/dev/r1dev3"}),
			withMounts(map[string]string{"/home/r1lib1": "/usr/r1lib1"}),
		),

*   Keras:

    *   `tf.keras.layers.Conv` now includes a public `convolution_op` method.
        This method can be used to simplify the implementation of Conv
        subclasses. There are two primary ways to use this new method. The first
        is to use the method directly in your own `call` method: `python class
        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):

      }
      func @_func(%input: tensor<2x112x112x12xf32>, %filter: tensor<7x7x3x64xf32>) {
        %filter_transform = "tf.Pad/tf.Transpose/tf.Reshape"(%filter): tensor<7x7x3x64xf32>) -> tensor<4x4x12x64xf32>
        %conv = "tf.Conv2D"(%input, %filter_transfrom) {strides = [1, 1, 1, 1]}: (tensor<2x112x112x12xf32>, tensor<4x4x12x64xf32>) -> tensor<2x112x112x64xf32>
      }
    }
    ```

				conv = conv1
			}
		}
		if Arch.LinkArch.Family == sys.ARM64 || Arch.LinkArch.Family == sys.Wasm || Arch.LinkArch.Family == sys.S390X || s.softFloat {
			if conv1, ok1 := uint64fpConvOpToSSA[twoTypes{s.concreteEtype(ft), s.concreteEtype(tt)}]; ok1 {
				conv = conv1
			}
		}

		if Arch.LinkArch.Family == sys.MIPS && !s.softFloat {
			if ft.Size() == 4 && ft.IsInteger() && !ft.IsSigned() {

	conn1 := <-conns

	// Start another request and grab its connection
	response2c := make(chan string, 1)
	go fetch(2, response2c)
	conn2 := <-conns

	// Send a response on connection 2.
	conn2.(*blockingRemoteAddrConn).addrs <- &net.TCPAddr{
		IP: net.ParseIP("12.12.12.12"), Port: 12}

	// ... and see it
	response2 := <-response2c

`channels_last_format`, see below for details.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_Conv2DOp : TF_Op<"Conv2D", [InferTensorType, Pure, TF_LayoutSensitiveInterface]> {
  let summary = [{
Computes a 2-D convolution given 4-D `input` and `filter` tensors.
  }];

  let description = [{