class_docstr: Concatenates slices, scalars and array-like objects along a given axis.
code: def __getitem__(
      self, key: _IndexType | tuple[_IndexType, ...]
  ) -> Array:
      key_tup: tuple[_IndexType, ...] = (
          key if isinstance(key, tuple) else (key,)
      )
  
      params = [self.axis, self.ndmin, self.trans1d, -1]
  
      directive = key_tup[0]
      if isinstance(directive, str):
          key_tup = key_tup[1:]
          # check two special cases: matrix directives
          if directive == "r":
              params[-1] = 0
          elif directive == "c":
              params[-1] = 1
          else:
              vec: list[Any] = directive.split(",")
              k = len(vec)
              if k < 4:
                  vec += params[k:]
              else:
                  # ignore everything after the first three comma-separated ints
                  vec = vec[:3] + [params[-1]]
              try:
                  params = list(map(int, vec))
              except ValueError as err:
                  raise ValueError(
                      f"could not understand directive {directive!r}"
                  ) from err
  
      axis, ndmin, trans1d, matrix = params
  
      output = []
      for item in key_tup:
          if isinstance(item, slice):
              newobj = _make_1d_grid_from_slice(
                  item, op_name=self.op_name
              )
              item_ndim = 0
          elif isinstance(item, str):
              raise ValueError(
                  "string directive must be placed at the beginning"
              )
          else:
              newobj = array(item, copy=False)
              item_ndim = newobj.ndim
  
          newobj = array(newobj, copy=False, ndmin=ndmin)
  
          if trans1d != -1 and ndmin - item_ndim > 0:
              shape_obj = tuple(range(ndmin))
              # Calculate number of left shifts, with overflow protection by mod
              num_lshifts = ndmin - abs(ndmin + trans1d + 1) % ndmin
              shape_obj = tuple(
                  shape_obj[num_lshifts:] + shape_obj[:num_lshifts]
              )
  
              newobj = transpose(newobj, shape_obj)
  
          output.append(newobj)
  
      res = concatenate(tuple(output), axis=axis)
  
      if matrix != -1 and res.ndim == 1:
          # insert 2nd dim at axis 0 or 1
          res = expand_dims(res, matrix)
  
      return res
  
imports: from __future__ import annotations
  import abc
  from collections.abc import Iterable
  from typing import Any, Union
  import jax
  from jax._src import core
  from jax._src.numpy.util import promote_dtypes
  from jax._src.numpy.lax_numpy import arange, array, concatenate, expand_dims, linspace, meshgrid, stack, transpose
  from jax._src.typing import Array, ArrayLike
  import numpy
label_desc: Use image node_id 1 for calling the _AxisConcat obj's underlying member method code with example usage: obj.__getitem__(key) and returns: res