You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: base.rs
    path of file: ./repos/burn/crates/burn-import/src/burn/node
    
    the code of the file until where you have to start completion: use super::unsqueeze::UnsqueezeNode;
use super::{
    avg_pool2d::AvgPool2dNode, batch_norm::BatchNormNode, binary::BinaryNode, clip::ClipNode,
    concat::ConcatNode, constant::ConstantNode, conv1d::Conv1dNode, conv2d::Conv2dNode,
    conv_transpose_2d::ConvTranspose2dNode, dropout::DropoutNode, gather::GatherNode,
    global_avg_pool::GlobalAvgPoolNode, linear::LinearNode, matmul::MatmulNode,
    max_pool2d::MaxPool2dNode, reshape::ReshapeNode, unary::UnaryNode,
};
use crate::burn::{BurnImports, Scope, Type};
use burn::backend::NdArray;
use burn::record::PrecisionSettings;
use proc_macro2::TokenStream;
use serde:
    
    fn test_codegen_two_nodes() {
        let mut graph = BurnGraph::<FullPrecisionSettings>::default();

        graph.register(MatmulNode::new(
            TensorType::new_float("tensor1", 4),
            TensorType::new_float("tensor2", 4),
            TensorType::new_float("tensor3", 4),
        ));
        graph.register(Conv2dNode::new(
            "conv2d",
            TensorType::new_float("tensor3", 4),
            TensorType::new_float("tensor4", 4),
            Data::from([2.]).serialize(),
            None,
            Conv2dConfig::new([3, 3], [3, 3]).with_padding(PaddingConfig2d::Valid),
        ));

        graph.register_input_output(
            vec!["tensor1".to_string(), "tensor2".to_string()],
            vec!["tensor4".to_string()],
        );

        let expected = quote! {
            use burn::{
                module::Module,
                tensor::{backend::Backend, Tensor},
            };
            use burn::nn::conv::Conv2dConfig;
            use burn::nn::conv::Conv2d;
            use burn::nn::PaddingConfig2d;

            #[derive(Module, Debug)]
            pub struct Model <B: Backend> {
                conv2d: Conv2d<B>,
                phantom: core::marker::PhantomData<B>,
            }

            impl<B: Backend> Model <B> {
                #[allow(unused_variables)]
                pub fn new_with(record: ModelRecord<B>) -> Self {
                    let conv2d = Conv2dConfig::new([3, 3], [3, 3])
                        .with_stride([1, 1])
                        .with_padding(PaddingConfig2d::Valid)
                        .with_dilation([1, 1])
                        .with_groups(1)
                        .with_bias(true)
                        .init_with(record.conv2d);

                    Self {
                        conv2d,
                        phantom: core::marker::PhantomData,
                    }
                }
                #[allow(clippy::let_and_return, clippy::approx_constant)]
                pub fn forward(
                    &self,
                    tensor1: Tensor<B, 4>,
                    tensor2: Tensor<B, 4>
                ) -> Tensor<B, 4> {
                    let tensor3 = tensor1.matmul(tensor2);
                    let tensor4 = self.conv2d.forward(tensor3);

                    tensor4
                }
            }
        };

        assert_tokens(graph.codegen(), expected);
    }