docstr: ivy.Container static method variant of ivy.batched_outer. This
  method simply wraps the function, and so the docstring for
  ivy.batched_outer also applies to this method with minimal changes.
  
  Parameters
  ----------
  tensors
      list of tensors of shape (n_samples, J1, ..., JN) ,
      (n_samples, K1, ..., KM) ...
  
  Returns
  -------
  outer product of tensors
      of shape (n_samples, J1, ..., JN, K1, ..., KM, ...)
  
  Examples
  --------
  >>> a = ivy.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
  >>> b = ivy.array([[[.1, .2], [.3, .4]], [[.5, .6], [.7, .8]]])
  >>> result = ivy.batched_outer(a, b)
  >>> print(result)
  ivy.array([[[[[0.1, 0.2],
        [0.30000001, 0.40000001]],
       [[0.2       , 0.40000001],
        [0.60000002, 0.80000001]]],
      [[[0.3       , 0.60000001],
        [0.90000004, 1.20000002]],
       [[0.40000001, 0.80000001],
        [1.20000005, 1.60000002]]]],
     [[[[2.5       , 3.00000012],
        [3.49999994, 4.00000006]],
       [[3.        , 3.60000014],
        [4.19999993, 4.80000007]]],
      [[[3.5       , 4.20000017],
        [4.89999992, 5.60000008]],
       [[4.        , 4.80000019],
        [5.5999999 , 6.4000001 ]]]]])
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code: def static_batched_outer(
      tensors: Sequence[
          Union[ivy.Array, ivy.NativeArray, ivy.Container]
      ],
      /,
      *,
      out: Optional[ivy.Array] = None,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.batched_outer. This
      method simply wraps the function, and so the docstring for
      ivy.batched_outer also applies to this method with minimal changes.
  
      Parameters
      ----------
      tensors
          list of tensors of shape (n_samples, J1, ..., JN) ,
          (n_samples, K1, ..., KM) ...
  
      Returns
      -------
      outer product of tensors
          of shape (n_samples, J1, ..., JN, K1, ..., KM, ...)
  
      Examples
      --------
      >>> a = ivy.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
      >>> b = ivy.array([[[.1, .2], [.3, .4]], [[.5, .6], [.7, .8]]])
      >>> result = ivy.batched_outer(a, b)
      >>> print(result)
      ivy.array([[[[[0.1, 0.2],
            [0.30000001, 0.40000001]],
           [[0.2       , 0.40000001],
            [0.60000002, 0.80000001]]],
          [[[0.3       , 0.60000001],
            [0.90000004, 1.20000002]],
           [[0.40000001, 0.80000001],
            [1.20000005, 1.60000002]]]],
         [[[[2.5       , 3.00000012],
            [3.49999994, 4.00000006]],
           [[3.        , 3.60000014],
            [4.19999993, 4.80000007]]],
          [[[3.5       , 4.20000017],
            [4.89999992, 5.60000008]],
           [[4.        , 4.80000019],
            [5.5999999 , 6.4000001 ]]]]])
      """
      return ContainerBase.cont_multi_map_in_function(
          "batched_outer",
          tensors,
          out=out,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
imports: from typing import Union, Optional, List, Dict, Tuple, Sequence, Literal
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 43 for calling the _ContainerWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.static_batched_outer() and returns: ContainerBase