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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def forward(self, x):
 Args: """

        head_outputs = [None] * self.t

        # Execute input layer
        if isinstance(self.input_layer, list):  # One input_module per task
            input_outputs = [mod(x) for mod, x in zip(self.input_layer, x)]
            x = torch.stack(input_outputs, dim=1)

            # Execute level-0 task heads from their respective input modules
            for t in self.task_map[0]:
                head = self.heads[t]
                head_outputs[t] = head(input_outputs[t])
        else:  # One input_module for all tasks
            x = self.input_layer(x)

            # Execute level-0 task heads from the single input module
            for t in self.task_map[0]:
                head = self.heads[t]
                head_outputs[t] = head(x)

        # Execute middle layers
        for i, layer in enumerate(self.middle_layers, start=1):
            x = layer(x)

            # Attach level-i task heads from the ith middle module
            for t in self.task_map[i]:
                head = self.heads[t]
                # Optionally include as input the predictions of parent tasks
                if self.config["pass_predictions"] and bool(self.task_graph.parents[t]):
                    task_input = [x]
                    for p in self.task_graph.parents[t]:
                        task_input.append(head_outputs[p])
                    task_input = torch.stack(task_input, dim=1)
                else:
                    task_input = x
                head_outputs[t] = head(task_input)
        return head_outputs