code: def __call__(self, batch):
      action = batch.action
      if self.num_actions is not None:
          assert len(action.shape) == 2, f"{action.shape}"
          # one hot makes shape (batch_size, stack_size, feature_dim)
          action = F.one_hot(batch.action, self.num_actions).float()
          # make shape to (batch_size, feature_dim, stack_size)
          action = action.transpose(1, 2)
  
      # For (1-dimensional) vector fields, RB returns (batch_size, state_dim)
      # or (batch_size, state_dim, stack_size).
      # We want these to all be (stack_size, batch_size, state_dim), so
      # unsqueeze the former case; Note this only happens for stack_size = 1.
      # Then, permute.
      permutation = [2, 0, 1]
      vector_fields = {
          "state": batch.state,
          "action": action,
          "next_state": batch.next_state,
      }
      for name, tensor in vector_fields.items():
          if len(tensor.shape) == 2:
              tensor = tensor.unsqueeze(2)
          assert (
              len(tensor.shape) == 3
          ), f"{name} has shape {tensor.shape}"
          vector_fields[name] = tensor.permute(permutation)
  
      # For scalar fields, RB returns (batch_size), or (batch_size, stack_size)
      # Do same as above, except transpose instead.
      scalar_fields = {
          "reward": batch.reward,
          "not_terminal": 1.0 - batch.terminal.float(),
      }
      for name, tensor in scalar_fields.items():
          if len(tensor.shape) == 1:
              tensor = tensor.unsqueeze(1)
          assert (
              len(tensor.shape) == 2
          ), f"{name} has shape {tensor.shape}"
          scalar_fields[name] = tensor.transpose(0, 1)
  
      # stack_size > 1, so let's pad not_terminal with 1's, since
      # previous states couldn't have been terminal..
      if scalar_fields["reward"].shape[0] > 1:
          batch_size = scalar_fields["reward"].shape[1]
          assert scalar_fields["not_terminal"].shape == (
              1,
              batch_size,
          ), f"{scalar_fields['not_terminal'].shape}"
          stacked_not_terminal = torch.ones_like(
              scalar_fields["reward"]
          )
          stacked_not_terminal[-1] = scalar_fields["not_terminal"]
          scalar_fields["not_terminal"] = stacked_not_terminal
  
      dict_batch = {
          "state": vector_fields["state"],
          "next_state": vector_fields["next_state"],
          "action": vector_fields["action"],
          "reward": scalar_fields["reward"],
          "not_terminal": scalar_fields["not_terminal"],
          "step": None,
          "time_diff": None,
      }
      return rlt.MemoryNetworkInput.from_dict(dict_batch)
  
imports: import inspect
  import logging
  from typing import Dict, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.types import Trajectory
  from reagent.preprocessing.types import InputColumn
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import rescale_actions
label_desc: Use image node_id 3 for calling the MemoryNetworkInputMaker obj's underlying member method code with example usage: obj.__call__(batch) and returns: rlt