code: def __init__(
      self,
      feature_pow: float,
      feature_min: float,
      feature_max: float,
      device: torch.device,
  ) -> None:
      super().__init__()
      self.feature_min = feature_min
      self.feature_max = feature_max
      self.feature_pow = feature_pow
      self.device = device
  
      param = torch.empty(
          [math.ceil(math.pow(feature_max, feature_pow)) + 2],
          device=device,
      )
      self.register_buffer("w", param)
  
imports: import math
  import unittest
  from typing import Dict, List, Tuple
  import hypothesis.strategies
  import torch
  from hypothesis import given, settings
  from torchrec import EmbeddingBagConfig, EmbeddingCollection, EmbeddingConfig, KeyedJaggedTensor
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel, ShardingType
  from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology
  from torchrec.distributed.planner.enumerators import EmbeddingEnumerator
  from torchrec.distributed.planner.shard_estimators import EmbeddingPerfEstimator, EmbeddingStorageEstimator
  from torchrec.distributed.quant_embeddingbag import QuantFeatureProcessedEmbeddingBagCollectionSharder
  from torchrec.distributed.quant_state import sharded_tbes_weights_spec, WeightSpec
  from torchrec.distributed.shard import _shard_modules
  from torchrec.distributed.sharding_plan import column_wise, construct_module_sharding_plan, placement, row_wise
  from torchrec.distributed.test_utils.infer_utils import assert_close, assert_weight_spec, create_cw_min_partition_constraints, create_test_model, KJTInputWrapper, model_input_to_forward_args, model_input_to_forward_args_kjt, prep_inputs, quantize, quantize_fpebc, shard_qebc, shard_qec, TestModelInfo, TestQuantEBCSharder
  from torchrec.distributed.test_utils.test_model import ModelInput
  from torchrec.distributed.types import ShardingEnv, ShardingPlan
  from torchrec.fx import symbolic_trace
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.modules.feature_processor_ import FeatureProcessorsCollection
  from torchrec.modules.fp_embedding_modules import FeatureProcessedEmbeddingBagCollection
label_desc: Use image node_id 1 to create a new TimeGapPoolingCollectionModule object from inherited base classes: FeatureProcessorsCollection with example: obj = TimeGapPoolingCollectionModule(feature_pow, feature_min, feature_max, device)