class_docstr: Represents a NumPy dataset for use with MLflow Tracking.
class_docstr_tok: ["Represents","a","NumPy","dataset","for","use","with","MLflow","Tracking","."]
docstr: Args:
      features: A numpy array or dictionary of numpy arrays containing dataset features.
      source: The source of the numpy dataset.
      targets: A numpy array or dictionary of numpy arrays containing dataset targets.
          Optional.
      name: The name of the dataset. E.g. "wiki_train". If unspecified, a name is
          automatically generated.
      digest: The digest (hash, fingerprint) of the dataset. If unspecified, a digest
          is automatically computed.
docstr_tok: ["Args",":","features",":","A","numpy","array","or","dictionary","of","numpy","arrays","containing","dataset","features",".","source",":","The","source","of","the","numpy","dataset",".","targets",":","A","numpy","array","or","dictionary","of","numpy","arrays","containing","dataset","targets",".","Optional",".","name",":","The","name","of","the","dataset",".","E.g",".","``","wiki_train","''",".","If","unspecified",",","a","name","is","automatically","generated",".","digest",":","The","digest","(","hash",",","fingerprint",")","of","the","dataset",".","If","unspecified",",","a","digest","is","automatically","computed","."]
code: def __init__(
      self,
      features: Union[np.ndarray, Dict[str, np.ndarray]],
      source: DatasetSource,
      targets: Union[np.ndarray, Dict[str, np.ndarray]] = None,
      name: Optional[str] = None,
      digest: Optional[str] = None,
  ):
      """
      Args:
          features: A numpy array or dictionary of numpy arrays containing dataset features.
          source: The source of the numpy dataset.
          targets: A numpy array or dictionary of numpy arrays containing dataset targets.
              Optional.
          name: The name of the dataset. E.g. "wiki_train". If unspecified, a name is
              automatically generated.
          digest: The digest (hash, fingerprint) of the dataset. If unspecified, a digest
              is automatically computed.
      """
      self._features = features
      self._targets = targets
      super().__init__(source=source, name=name, digest=digest)
  
imports: import json
  import logging
  from functools import cached_property
  from typing import Any, Dict, Optional, Union
  import numpy
  from mlflow.data.dataset import Dataset
  from mlflow.data.dataset_source import DatasetSource
  from mlflow.data.digest_utils import compute_numpy_digest
  from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin, PyFuncInputsOutputs
  from mlflow.data.schema import TensorDatasetSchema
  from mlflow.models.evaluation.base import EvaluationDataset
  from mlflow.types.utils import _infer_schema
  from mlflow.utils.annotations import experimental
label_desc: Use image node_id 1 to create a new NumpyDataset object from inherited base classes: Dataset, PyFuncConvertibleDatasetMixin with example: obj = NumpyDataset(features, source, targets, name, digest)