code: def from_indices_shape(cls, indices, shape) -> NDIndexer:
      if not isinstance(indices, tuple):
          indices = (indices,)
      if len(indices) == 1 and indices[0] is ...:
          indices = (slice(None),) * len(shape)
      if any(idx is ... for idx in indices):
          # TODO(sharadmv,mattjj): support patterns that include ellipsis in them
          #                        e.g. x[0, ..., 1].
          raise NotImplementedError(
              "Ellipsis in indexer not supported yet."
          )
      if len(indices) > len(shape):
          raise ValueError(
              "`indices` must not be longer than `shape`: "
              f"{indices=}, {shape=}"
          )
      # Pad out indices with slice(None)
      indices = [*indices, *[slice(None)] * (len(shape) - len(indices))]
      # Convert all `slice`s to `Slice`s
      indices = tuple(
          Slice.from_slice(i, s) if isinstance(i, slice) else i
          for i, s in zip(indices, shape)
      )
      is_int_indexing = [not isinstance(i, Slice) for i in indices]
      other_indexers, int_indexers = partition_list(
          is_int_indexing, indices
      )
      indexer_shapes = [core.get_aval(i).shape for i in int_indexers]
      if indexer_shapes:
          try:
              bcast_shape = np.broadcast_shapes(*indexer_shapes)
          except ValueError as e:
              # Raise a nicer error than the NumPy one.
              raise ValueError(
                  "Cannot broadcast shapes for indexing: "
                  f"{tuple(a for a in indexer_shapes)}"
              ) from e
      else:
          bcast_shape = ()
      # Here we use the `broadcast_to` primitive instead of composing lax
      # primitives together because it is easier to lower in targets like
      # Triton/Mosaic.
      from jax._src.state import (
          primitives as sp,
      )  # pytype: disable=import-error
  
      int_indexers = [
          sp.broadcast_to(i, bcast_shape) for i in int_indexers
      ]
      indices = merge_lists(
          is_int_indexing, other_indexers, int_indexers
      )
      return NDIndexer(
          tuple(indices), shape, bcast_shape, validate=True
      )
  
imports: from __future__ import annotations
  import dataclasses
  from typing import Any, Union
  from jax._src import core
  from jax._src import tree_util
  from jax._src.typing import Array
  from jax._src.util import merge_lists
  from jax._src.util import partition_list
  import numpy
label_desc: Use image node_id 4 for calling the NDIndexer obj's underlying member method code with example usage: obj.from_indices_shape(cls, indices, shape) and returns: NDIndexer