class_docstr: Represents a HuggingFace dataset for use with MLflow Tracking.
class_docstr_tok: ["Represents","a","HuggingFace","dataset","for","use","with","MLflow","Tracking","."]
docstr: Summary statistics for the Hugging Face dataset, including the number of rows,
  size, and size in bytes.
docstr_tok: ["Summary","statistics","for","the","Hugging","Face","dataset",",","including","the","number","of","rows",",","size",",","and","size","in","bytes","."]
code: def profile(self) -> Optional[Any]:
      """
      Summary statistics for the Hugging Face dataset, including the number of rows,
      size, and size in bytes.
      """
      return {
          "num_rows": self._ds.num_rows,
          "dataset_size": self._ds.dataset_size,
          "size_in_bytes": self._ds.size_in_bytes,
      }
  
imports: import json
  import logging
  from functools import cached_property
  from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Sequence, Union
  from mlflow.data.dataset import Dataset
  from mlflow.data.digest_utils import compute_pandas_digest
  from mlflow.data.huggingface_dataset_source import HuggingFaceDatasetSource
  from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin, PyFuncInputsOutputs
  from mlflow.exceptions import MlflowException
  from mlflow.models.evaluation.base import EvaluationDataset
  from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, INVALID_PARAMETER_VALUE
  from mlflow.types import Schema
  from mlflow.types.utils import _infer_schema
  from mlflow.utils.annotations import experimental
label_desc: Use image node_id 7 for calling the HuggingFaceDataset obj's underlying member method code with example usage: obj.profile() and returns: dict