docstr: Runs a forward pass & backward pass, clips gradients if needed and accumulates them into the accumulation
  placeholders
  :param additional_fetches: Optional tensors to fetch during gradients calculation
  :param inputs: The input batch for the network
  :param targets: The targets corresponding to the input batch
  :param importance_weights: A coefficient for each sample in the batch, which will be used to rescale the loss
                             error of this sample. If it is not given, the samples losses won't be scaled
  :param no_accumulation: If is set to True, the gradients in the accumulated gradients placeholder will be
                          replaced by the newely calculated gradients instead of accumulating the new gradients.
                          This can speed up the function runtime by around 10%.
  :return: A list containing the total loss and the individual network heads losses
code: def accumulate_gradients(
      self,
      inputs,
      targets,
      additional_fetches=None,
      importance_weights=None,
      no_accumulation=False,
  ):
      """
      Runs a forward pass & backward pass, clips gradients if needed and accumulates them into the accumulation
      placeholders
      :param additional_fetches: Optional tensors to fetch during gradients calculation
      :param inputs: The input batch for the network
      :param targets: The targets corresponding to the input batch
      :param importance_weights: A coefficient for each sample in the batch, which will be used to rescale the loss
                                 error of this sample. If it is not given, the samples losses won't be scaled
      :param no_accumulation: If is set to True, the gradients in the accumulated gradients placeholder will be
                              replaced by the newely calculated gradients instead of accumulating the new gradients.
                              This can speed up the function runtime by around 10%.
      :return: A list containing the total loss and the individual network heads losses
      """
  
      if self.accumulated_gradients is None:
          self.reset_accumulated_gradients()
  
      # feed inputs
      if additional_fetches is None:
          additional_fetches = []
      feed_dict = self.create_feed_dict(inputs)
  
      # feed targets
      targets = force_list(targets)
      for placeholder_idx, target in enumerate(targets):
          feed_dict[self.targets[placeholder_idx]] = target
  
      # feed importance weights
      importance_weights = force_list(importance_weights)
      for placeholder_idx, target_ph in enumerate(targets):
          if (
              len(importance_weights) <= placeholder_idx
              or importance_weights[placeholder_idx] is None
          ):
              importance_weight = np.ones(target_ph.shape[0])
          else:
              importance_weight = importance_weights[placeholder_idx]
          importance_weight = np.reshape(
              importance_weight,
              (-1,) + (1,) * (len(target_ph.shape) - 1),
          )
  
          feed_dict[
              self.importance_weights[placeholder_idx]
          ] = importance_weight
  
      if self.optimizer_type != "LBFGS":
          # feed the lstm state if necessary
          if self.middleware.__class__.__name__ == "LSTMMiddleware":
              # we can't always assume that we are starting from scratch here can we?
              feed_dict[self.middleware.c_in] = self.middleware.c_init
              feed_dict[self.middleware.h_in] = self.middleware.h_init
  
          fetches = self.train_fetches + additional_fetches
          if self.ap.visualization.tensorboard:
              fetches += [self.merged]
  
          # get grads
          result = self.sess.run(fetches, feed_dict=feed_dict)
          if (
              hasattr(self, "train_writer")
              and self.train_writer is not None
          ):
              self.train_writer.add_summary(
                  result[-1], self.sess.run(self.global_step)
              )
  
          # extract the fetches
          norm_unclipped_grads, grads, total_loss, losses = result[:4]
          if self.middleware.__class__.__name__ == "LSTMMiddleware":
              (self.curr_rnn_c_in, self.curr_rnn_h_in) = result[4]
          fetched_tensors = []
          if len(additional_fetches) > 0:
              fetched_tensors = result[
                  self.additional_fetches_start_idx : self.additional_fetches_start_idx
                  + len(additional_fetches)
              ]
  
          # accumulate the gradients
          for idx, grad in enumerate(grads):
              if no_accumulation:
                  self.accumulated_gradients[idx] = grad
              else:
                  self.accumulated_gradients[idx] += grad
  
          return (
              total_loss,
              losses,
              norm_unclipped_grads,
              fetched_tensors,
          )
  
      else:
          self.optimizer.minimize(
              session=self.sess, feed_dict=feed_dict
          )
  
          return [0]
  
imports: import os
  import time
  from typing import Any, List, Tuple, Dict
  import numpy
  import tensorflow
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.tensorflow_components.savers import GlobalVariableSaver
  from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import GradientClippingMethod
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list, start_shell_command_and_wait
label_desc: Use image node_id 11 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj.accumulate_gradients(inputs, targets, additional_fetches, importance_weights, no_accumulation) and returns: total_loss,  losses,  norm_unclipped_grads,  fetched_tensors, list