code: def _compute_cost_and_update_grads(
      cost_fn,
      order,
      batch,
      variables,
      outer_v,
      keep_outer_v,
      average_across_steps_or_final,
      all_grads,
      unique_outer,
      batched,
      num_tasks,
  ):
      """Compute cost and update gradients.
  
      This function computes the cost and updates gradients for optimization.
  
      Parameters
      ----------
      cost_fn : function
          The cost function.
      order : int
          The order of computation.
      batch : object
          The batch data.
      variables : ivy.Container
          The variables for optimization.
      outer_v : object
          Outer variable.
      keep_outer_v : bool
          Whether to keep outer variable.
      average_across_steps_or_final : bool
          Whether to average across steps or final.
      all_grads : list
          List to accumulate gradients.
      unique_outer : bool
          Whether outer variables are unique.
      batched : bool
          Whether the data is batched.
      num_tasks : int
          Number of tasks.
  
      Returns
      -------
      object
          The computed cost.
  
      Examples
      --------
      >>> # Example usage here
      >>> pass
      """
      if order == 1:
  
          def cost_fn_with_variable(v):
              return cost_fn(
                  batch,
                  v=variables.cont_set_at_key_chains(v)
                  if unique_outer
                  else v,
              )
  
          cost, inner_grads = ivy.execute_with_gradients(
              cost_fn_with_variable,
              (
                  variables.cont_at_key_chains(
                      outer_v, ignore_none=True
                  )
                  if keep_outer_v
                  else variables.cont_prune_key_chains(
                      outer_v, ignore_none=True
                  )
              ),
              retain_grads=False,
          )
  
          var = (
              variables.cont_at_key_chains(outer_v, ignore_none=True)
              if keep_outer_v
              else variables.cont_prune_key_chains(
                  outer_v, ignore_none=True
              )
          )
  
          inner_grads = ivy.Container(
              {
                  k: ivy.zeros_like(v)
                  if k not in inner_grads
                  else inner_grads[k]
                  for k, v in var.cont_to_iterator()
              }
          )
  
          if batched:
              inner_grads = ivy.multiply(inner_grads, num_tasks)
  
          if average_across_steps_or_final:
              all_grads.append(inner_grads)
      else:
          cost = cost_fn(batch, v=variables)
  
      return cost
  
file_path: ivy/ivy/functional/ivy/meta.py
imports: import ivy
  from ivy.func_wrapper import handle_array_function
  from ivy.functional.ivy.gradients import gradient_descent_update
  from ivy.utils.exceptions import handle_exceptions
  from typing import Optional, Union, Callable, Tuple, Any
label_desc: Use image node_id 1 for calling a global function with example usage: _compute_cost_and_update_grads(cost_fn, order, batch, variables, outer_v, keep_outer_v, average_across_steps_or_final, all_grads, unique_outer, batched, num_tasks) and returns: cost, cost_fn