code: def get_even_indices_pruning_ebc_dict(
      table_key: str, num_embeddings: int
  ) -> Dict[str, torch.Tensor]:
      pruning_ebc_dict: Dict[str, torch.Tensor] = {}
      remapping_indices = torch.full(
          fill_value=-1, size=[num_embeddings], dtype=torch.int32
      )
  
      # Prune element at even index position
      for i in range(200):
          if i % 2 == 0:
              continue
          remapping_indices[i] = i // 2
  
      pruning_ebc_dict[table_key] = remapping_indices
      return pruning_ebc_dict
  
file_path: torchrec/torchrec/distributed/tests/test_quant_pruning.py
imports: import io
  import unittest
  from typing import Dict, List, Tuple
  import torch
  from torchrec.distributed.embedding_types import ShardingType
  from torchrec.distributed.quant_state import sharded_tbes_weights_spec, WeightSpec
  from torchrec.distributed.test_utils.infer_utils import assert_close, assert_weight_spec, create_test_model, create_test_model_ebc_only_no_quantize, model_input_to_forward_args, prep_inputs, quantize, shard_qebc
  from torchrec.fx import symbolic_trace
  from torchrec.quant.embedding_modules import MODULE_ATTR_EMB_CONFIG_NAME_TO_PRUNING_INDICES_REMAPPING_DICT
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 2 for calling a global function with example usage: get_even_indices_pruning_ebc_dict(table_key, num_embeddings) and returns: pruning_ebc_dict