code: def pack(
      tensors: Sequence[Tensor], pattern: str
  ) -> Tuple[Tensor, List[Shape]]:
      n_axes_before, n_axes_after, min_axes = analyze_pattern(
          pattern, "pack"
      )
      xp = tensors[0].__array_namespace__()
  
      reshaped_tensors: List[Tensor] = []
      packed_shapes: List[Shape] = []
      for i, tensor in enumerate(tensors):
          shape = tensor.shape
          if len(shape) < min_axes:
              raise EinopsError(
                  f"packed tensor #{i} (enumeration starts with 0) has shape {shape}, "
                  f"while pattern {pattern} assumes at least {min_axes} axes"
              )
          axis_after_packed_axes = len(shape) - n_axes_after
          packed_shapes.append(
              shape[n_axes_before:axis_after_packed_axes]
          )
          reshaped_tensors.append(
              xp.reshape(
                  tensor,
                  (
                      *shape[:n_axes_before],
                      -1,
                      *shape[axis_after_packed_axes:],
                  ),
              )
          )
  
      return (
          xp.concat(reshaped_tensors, axis=n_axes_before),
          packed_shapes,
      )
  
file_path: einops/einops/array_api.py
imports: from typing import List, Tuple, Sequence
  from .einops import Tensor, Reduction, EinopsError, _prepare_transformation_recipe, _apply_recipe_array_api
  from .packing import analyze_pattern, prod
label_desc: Use image node_id 5 for calling a global function with example usage: pack(tensors, pattern) and returns: xp,  packed_shapes