The implementation of** tf.nn.conv2d() **is written in **C++**, which invokes optimized code using Eigen or the cuDNN library (for GPU).

The chain of functions from tf.nn.conv2d() are Python functions for building a TensorFlow graph, but these do not invoke the implementation. In TensorFlow, you first build a symbolic graph, then execute it.

The implementation of tf.nn.conv2d() is only executed happens when you call **Session.run(),** passing a Tensor depends on the result of some convolution.

**For example:**

input = tf.placeholder(tf.float32)

filter = tf.Variable(tf.truncated_normal([5, 5, 3, 32], stddev=0.1)

conv = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='SAME')

result = sess.run(conv, feed_dict={input: ...})

If you invoke sess.run(...), then TensorFlow will run all the ops that are needed to compute the value of conv, including the convolution itself. The path from here to the implementation is quite complicated, but goes through the following steps:

**sess.run()** calls the TensorFlow backend to fetch the value of conv.

The backend prunes the computation graph so that nodes must be executed and places the nodes on the appropriate devices.

Each device is instructed to execute its subgraph, using an executor.

The executor eventually invokes the TensorFlow:: OpKernel that simply corresponds to the convolution operator, by calling its **Compute()** method.

The "Conv2D" OpKernel is implemented here, and its Compute() method is here. Because this op is performance-critical for many workloads, then the implementation is quite complicated, but the basic idea is that the computation is the Eigen Tensor library or cuDNN's optimized GPU implementation.

Hope this answer helps you! Questions about Tensorflow are frequently asked, thus studying this will enable one to crack Machine Learning Interview Questions as well. Thus, Machine Learning would be required for a better results.