code: def pred_contribs(
      model: XGBModel,
      data: ArrayLike,
      base_margin: Optional[ArrayLike] = None,
      strict_shape: bool = False,
  ) -> np.ndarray:
      """Predict contributions with data with the full model."""
      iteration_range = model._get_iteration_range(None)
      data_dmatrix = DMatrix(
          data,
          base_margin=base_margin,
          missing=model.missing,
          nthread=model.n_jobs,
          feature_types=model.feature_types,
          enable_categorical=model.enable_categorical,
      )
      return model.get_booster().predict(
          data_dmatrix,
          pred_contribs=True,
          validate_features=False,
          iteration_range=iteration_range,
          strict_shape=strict_shape,
      )
  
imports: from collections import defaultdict, namedtuple
  from typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from scipy.sparse import csr_matrix
  from xgboost import DataIter, DMatrix, QuantileDMatrix, XGBModel
  from xgboost.compat import concat
  from .._typing import ArrayLike
  from .utils import get_logger
label_desc: Use image node_id 7 for calling a global function with example usage: pred_contribs(model, data, base_margin, strict_shape) and returns: model