code: def to_merged_sparse_mt(
      vds: "VariantDataset", *, ref_allele_function=None
  ) -> "MatrixTable":
      """Creates a single, merged sparse :class:`.MatrixTable` from the split
      :class:`.VariantDataset` representation.
  
      Parameters
      ----------
      vds : :class:`.VariantDataset`
          Dataset in VariantDataset representation.
  
      Returns
      -------
      :class:`.MatrixTable`
          Dataset in the merged sparse MatrixTable representation.
      """
      rht = vds.reference_data.localize_entries(
          "_ref_entries", "_ref_cols"
      )
      vht = vds.variant_data.localize_entries(
          "_var_entries", "_var_cols"
      )
  
      # drop 'alleles' key for join
      vht = vht.key_by("locus")
  
      merged_schema = {}
      for e in vds.reference_data.entry:
          merged_schema[e] = vds.reference_data[e].dtype
  
      for e in vds.variant_data.entry:
          if e in merged_schema:
              if not merged_schema[e] == vds.variant_data[e].dtype:
                  raise TypeError(
                      f"cannot unify field {e!r}: {merged_schema[e]}, {vds.variant_data[e].dtype}"
                  )
          else:
              merged_schema[e] = vds.variant_data[e].dtype
  
      ht = vht.join(rht, how="outer").drop("_ref_cols")
  
      def merge_arrays(r_array, v_array):
          def rewrite_ref(r):
              ref_block_selector = {}
              for k, t in merged_schema.items():
                  if k == "LA":
                      ref_block_selector[k] = hl.literal([0])
                  elif k in ("LGT", "GT"):
                      ref_block_selector[k] = hl.call(0, 0)
                  else:
                      ref_block_selector[k] = (
                          r[k] if k in r else hl.missing(t)
                      )
              return r.select(**ref_block_selector)
  
          def rewrite_var(v):
              return v.select(
                  **{
                      k: v[k] if k in v else hl.missing(t)
                      for k, t in merged_schema.items()
                  }
              )
  
          return (
              hl.case()
              .when(hl.is_missing(r_array), v_array.map(rewrite_var))
              .when(hl.is_missing(v_array), r_array.map(rewrite_ref))
              .default(
                  hl.zip(r_array, v_array).map(
                      lambda t: hl.coalesce(
                          rewrite_var(t[1]), rewrite_ref(t[0])
                      )
                  )
              )
          )
  
      if ref_allele_function is None:
          rg = ht.locus.dtype.reference_genome
          if "ref_allele" in ht.row:
  
              def ref_allele_function(ht):
                  return ht.ref_allele
  
          elif rg.has_sequence():
  
              def ref_allele_function(ht):
                  return ht.locus.sequence_context()
  
              info(
                  "to_merged_sparse_mt: using locus sequence context to fill in reference alleles at monomorphic loci."
              )
          else:
              raise ValueError(
                  "to_merged_sparse_mt: in order to construct a ref allele for reference-only sites, "
                  "either pass a function to fill in reference alleles (e.g. ref_allele_function=lambda locus: hl.missing('str'))"
                  " or add a sequence file with 'hl.get_reference(RG_NAME).add_sequence(FASTA_PATH)'."
              )
      ht = ht.select(
          alleles=hl.coalesce(
              ht["alleles"], hl.array([ref_allele_function(ht)])
          ),
          # handle cases where vmt is not keyed by alleles
          **{
              k: ht[k]
              for k in vds.variant_data.row_value
              if k != "alleles"
          },
          _entries=merge_arrays(ht["_ref_entries"], ht["_var_entries"]),
      )
      ht = ht._key_by_assert_sorted("locus", "alleles")
      return ht._unlocalize_entries(
          "_entries", "_var_cols", list(vds.variant_data.col_key)
      )
  
imports: from typing import Sequence
  import hail
  from hail import ir
  from hail.expr import expr_any, expr_array, expr_interval, expr_locus, expr_str, expr_bool
  from hail.matrixtable import MatrixTable
  from hail.methods.misc import require_first_key_field_locus
  from hail.table import Table
  from hail.typecheck import sequenceof, typecheck, nullable, oneof, enumeration, func_spec, dictof
  from hail.utils.java import Env, info, warning
  from hail.utils.misc import divide_null, new_temp_file, wrap_to_list
  from hail.vds.variant_dataset import VariantDataset
label_desc: Use image node_id 3 for calling a global function with example usage: to_merged_sparse_mt(vds) and returns: ht, hl, r, v, ht, ht