code: def get_grid_mapping(
      self, in_avals, in_tree, out_avals, out_tree
  ) -> tuple[tuple[jax_core.AbstractValue, ...], GridMapping]:
      all_avals = tree_util.tree_unflatten(in_tree, in_avals)
      flat_scratch_shapes, scratch_tree = tree_util.tree_flatten(
          self.scratch_shapes
      )
      flat_scratch_avals = map(_make_aval, flat_scratch_shapes)
      scalar_avals, unflat_in_avals = split_list(
          all_avals, [self.num_scalar_prefetch]
      )
      flat_scalar_avals, scalar_tree = tree_util.tree_flatten(
          scalar_avals
      )
      num_flat_scalar_prefetch = len(flat_scalar_avals)
      in_avals, in_avals_tree = tree_util.tree_flatten(
          tuple(unflat_in_avals)
      )
      flat_in_specs, flat_out_specs = self._get_in_out_specs(
          in_avals, in_avals_tree, out_avals, out_tree
      )
      (
          in_specs,
          in_ref_avals,
          out_specs,
          out_ref_avals,
      ) = pallas_core._get_ref_avals(
          self.grid, in_avals, flat_in_specs, out_avals, flat_out_specs
      )
      scalar_ref_avals = [
          state.shaped_array_ref(aval.shape, aval.dtype)
          for aval in flat_scalar_avals
      ]
      grid_avals = [jax_core.ShapedArray((), jnp.dtype("int32"))] * len(
          self.grid
      )
      # Create args, kwargs pytree def
      index_map_in_tree = tree_util.tree_structure(
          ((*grid_avals, *scalar_avals), {})
      )
      in_block_mappings = map(
          partial(
              _convert_block_spec_to_block_mapping,
              (*grid_avals, *scalar_ref_avals),
              in_tree=index_map_in_tree,
          ),
          in_specs,
          in_ref_avals,
      )
      out_block_mappings = map(
          partial(
              _convert_block_spec_to_block_mapping,
              (*grid_avals, *scalar_ref_avals),
              in_tree=index_map_in_tree,
          ),
          out_specs,
          out_ref_avals,
      )
      grid_mapping = GridMapping(
          grid=self.grid,
          block_mappings=(*in_block_mappings, *out_block_mappings),
          mapped_dims=(),
          num_index_operands=num_flat_scalar_prefetch,
          num_scratch_operands=len(flat_scratch_avals),
      )
      jaxpr_scalar_ref_avals = tree_util.tree_unflatten(
          scalar_tree, scalar_ref_avals
      )
      jaxpr_in_ref_avals = tree_util.tree_unflatten(
          in_avals_tree, in_ref_avals
      )
      jaxpr_scratch_avals = tree_util.tree_unflatten(
          scratch_tree, flat_scratch_avals
      )
      if not isinstance(jaxpr_scratch_avals, (tuple, list)):
          jaxpr_scratch_avals = (jaxpr_scratch_avals,)
      jaxpr_in_avals = (*jaxpr_scalar_ref_avals, *jaxpr_in_ref_avals)
      jaxpr_out_avals = tree_util.tree_unflatten(
          out_tree, out_ref_avals
      )
      if not isinstance(jaxpr_out_avals, (tuple, list)):
          jaxpr_out_avals = (jaxpr_out_avals,)
      return (
          *jaxpr_in_avals,
          *jaxpr_out_avals,
          *jaxpr_scratch_avals,
      ), grid_mapping
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import dataclasses
  import enum
  import functools
  from typing import Any
  from jax._src import core
  from jax._src import dtypes
  from jax._src import state
  from jax._src import tree_util
  from jax._src import util
  import jax.numpy
  from jax._src.pallas import core
label_desc: Use image node_id 2 for calling the PrefetchScalarGridSpec obj's underlying member method code with example usage: obj.get_grid_mapping(in_avals, in_tree, out_avals, out_tree) and returns: unknown,  unknown,  unknown,  grid_mapping