chore: update submodules (#178)

Co-authored-by: vfdev-5 <vfdev-5@users.noreply.github.com>

Author: github-actions[bot]

src/how-to-guides/02-convert-pytorch-to-ignite.md

@@ -122,7 +122,7 @@ Accuracy().attach(evaluator, "accuracy")
 
 ## Organizing code into Events and Handlers
 
-Next, we need to identify any code that is triggered when an event occurs. Examples of events can be the start of an iteration, completion of an epoch, or even the start of backprop. We already provide some predefined events (complete list [here](https://pytorch.org/ignite/generated/ignite.engine.events.Events.html#ignite.engine.events.Events)) however we can also create custom ones (refer [here](https://pytorch.org/ignite/concepts.html#custom-events)). We move the event-specific code to different handlers (named functions, lambdas, class functions) which are attached to these events and executed whenever a specific event happens. Here are some common handlers:
+Next, we need to identify any code that is triggered when an event occurs. Examples of events can be the start of an iteration, completion of an epoch, or even the start of backprop. We already provide some predefined events (complete list [here](https://pytorch.org/ignite/generated/ignite.engine.events.Events.html#ignite.engine.events.Events)) however we can also create custom ones (refer [here](https://pytorch-ignite.ai/concepts/02-events-and-handlers#custom-events). We move the event-specific code to different handlers (named functions, lambdas, class functions) which are attached to these events and executed whenever a specific event happens. Here are some common handlers:
 
 ### Running `evaluator`
 

src/how-to-guides/03-time-profiling.md

@@ -15,8 +15,8 @@ tags:
 This example demonstrates how you can get the time breakdown for:
 - Individual epochs during training
 - Total training time
-- Individual [`Events`](https://pytorch.org/ignite/concepts.html#events-and-handlers)
-- All [`Handlers`](https://pytorch.org/ignite/concepts.html#handlers) correspoding to an `Event`
+- Individual [`Events`](https://pytorch-ignite.ai/concepts/02-events-and-handlers#events)
+- All [`Handlers`](https://pytorch-ignite.ai/concepts/02-events-and-handlers#handlers) correspoding to an `Event`
 - Individual `Handlers`
 - Data loading and Data processing.
 
@@ -251,7 +251,7 @@ basic_profiler.print_results(results);
 
 ## Handler-based profiling using `HandlersTimeProfiler`
 
-We can overcome the above problem by using [`HandlersTimeProfiler`](https://pytorch.org/ignite/generated/ignite.handlers.time_profilers.HandlersTimeProfiler.html#handlerstimeprofiler) which gives us only the necessary information. We can also calculate the time taken by handlers attached to [`Custom Events`](https://pytorch.org/ignite/concepts.html#custom-events), which was not previously possible, via this.
+We can overcome the above problem by using [`HandlersTimeProfiler`](https://pytorch.org/ignite/generated/ignite.handlers.time_profilers.HandlersTimeProfiler.html#handlerstimeprofiler) which gives us only the necessary information. We can also calculate the time taken by handlers attached to [`Custom Events`](https://pytorch-ignite.ai/concepts/02-events-and-handlers#custom-events), which was not previously possible, via this.
 
 
 ```python

src/how-to-guides/08-custom-events.md

@@ -9,7 +9,7 @@ tags:
 ---
 # How to create Custom Events based on Forward or Backward Pass
 
-This guide demonstrates how you can create [custom events](https://pytorch.org/ignite/concepts.html#custom-events) that depend on the loss calculated and backward pass.
+This guide demonstrates how you can create [custom events](https://pytorch-ignite.ai/concepts/02-events-and-handlers#custom-events) that depend on the loss calculated and backward pass.
 
 In this example, we will be using a ResNet18 model on the MNIST dataset. The base code is the same as used in the [Getting Started Guide](https://pytorch-ignite.ai/tutorials/getting-started/).
 

src/tutorials/beginner/02-transformers-text-classification.md

@@ -135,7 +135,7 @@ model.to(device)
 
 ## Create Trainer
 
-Ignite's [`Engine`](https://pytorch.org/ignite/concepts.html#engine) allows users to define a `process_function` to process a given batch of data. This function is applied to all the batches of the dataset. This is a general class that can be applied to train and validate models. A `process_function` has two parameters `engine` and `batch`.
+Ignite's [`Engine`](https://pytorch-ignite.ai/concepts/01-engine/) allows users to define a `process_function` to process a given batch of data. This function is applied to all the batches of the dataset. This is a general class that can be applied to train and validate models. A `process_function` has two parameters `engine` and `batch`.
 
 The code for processing a batch of training data in the tutorial is as follows:
 
@@ -192,7 +192,7 @@ trainer = Engine(train_step)
 
 The `lr_scheduler` we defined perviously was a handler. 
 
-[Handlers](https://pytorch.org/ignite/concepts.html#handlers) can be any type of function (lambda functions, class methods, etc). On top of that, Ignite provides several built-in handlers to reduce redundant code. We attach these handlers to engine which is triggered at a specific [event](https://pytorch.org/ignite/concepts.html#events-and-handlers). These events can be anything like the start of an iteration or the end of an epoch. [Here](https://pytorch.org/ignite/generated/ignite.engine.events.Events.html#events) is a complete list of built-in events.
+[Handlers](https://pytorch-ignite.ai/concepts/02-events-and-handlers/#handlers) can be any type of function (lambda functions, class methods, etc). On top of that, Ignite provides several built-in handlers to reduce redundant code. We attach these handlers to engine which is triggered at a specific [event](https://pytorch-ignite.ai/concepts/02-events-and-handlers/). These events can be anything like the start of an iteration or the end of an epoch. [Here](https://pytorch.org/ignite/generated/ignite.engine.events.Events.html#events) is a complete list of built-in events.
 
 Therefore, we will attach the `lr_scheduler` (handler) to the `trainer` (`engine`) via [`add_event_handler()`](https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html#ignite.engine.engine.Engine.add_event_handler) so it can be triggered at `Events.ITERATION_STARTED` (start of an iteration) automatically.
 
@@ -256,7 +256,7 @@ for batch in eval_dataloader:
 
 Finally, we return the predictions and the actual labels so that we can compute the metrics.
 
-You will notice that we did not compute the metrics in `evaluate_step()`. This is because Ignite provides built-in [metrics](https://pytorch.org/ignite/concepts.html#metrics) which we can later attach to the engine.
+You will notice that we did not compute the metrics in `evaluate_step()`. This is because Ignite provides built-in [metrics](https://pytorch-ignite.ai/concepts/04-metrics/) which we can later attach to the engine.
 
 **Note:** Ignite suggests attaching metrics to evaluators and not trainers because during the training the model parameters are constantly changing and it is best to evaluate model on a stationary model. This information is important as there is a difference in the functions for training and evaluating. Training returns a single scalar loss. Evaluating returns `y_pred` and `y` as that output is used to calculate metrics per batch for the entire dataset.
 

src/tutorials/intermediate/03-reinforcement-learning.md

@@ -170,7 +170,7 @@ timesteps = range(10000)
 
 ## Create Trainer
 
-Ignite's [`Engine`](https://pytorch.org/ignite/concepts.html#engine) allows users to define a `process_function` to run one episode. We select an action from the policy, then take the action through `step()` and finally increment our reward. If the problem is solved, we terminate training and save the `timestep`.
+Ignite's [`Engine`](https://pytorch-ignite.ai/concepts/01-engine/) allows users to define a `process_function` to run one episode. We select an action from the policy, then take the action through `step()` and finally increment our reward. If the problem is solved, we terminate training and save the `timestep`.
 
 > An episode is an instance of a game (or life of a game). If the game ends or life decreases, the episode ends. Step, on the other hand, is the time or some discrete value which increases monotonically in an episode. With each change in the state of the game, the value of step increases until the game ends.