class_docstr: Base class for Datasets.
docstr: Load a dataset from a HDF5 Group or initialize a new Dataset.
  
  Args:
      bind: The HDF5 group that contains this dataset. If None, a new
          group will be created in memory. Any attributes that already exist
          in ``bind`` will be loaded into this dataset.
      data_name: String describing the type of data this datasets contains, e.g
          'qchem' for quantum chemistry. Defaults to the data name defined by
          the class, this is 'generic' for base datasets.
      identifiers: Tuple of names of attributes of this dataset that will serve
          as its parameters
      **attrs: Attributes to add to this dataset.
docstr_tok: ["Load","a","dataset","from","a","HDF5","Group","or","initialize","a","new","Dataset",".","Args",":","bind",":","The","HDF5","group","that","contains","this","dataset",".","If","None",",","a","new","group","will","be","created","in","memory",".","Any","attributes","that","already","exist","in","``","bind","``","will","be","loaded","into","this","dataset",".","data_name",":","String","describing","the","type","of","data","this","datasets","contains",",","e.g","'qchem","'","for","quantum","chemistry",".","Defaults","to","the","data","name","defined","by","the","class",",","this","is","'generic","'","for","base","datasets",".","identifiers",":","Tuple","of","names","of","attributes","of","this","dataset","that","will","serve","as","its","parameters","*","*","attrs",":","Attributes","to","add","to","this","dataset","."]
code: def __init__(
      self,
      bind: Optional[HDF5Group] = None,
      *,
      data_name: Optional[str] = None,
      identifiers: Optional[Tuple[str, ...]] = None,
      **attrs: Any,
  ):
      """
      Load a dataset from a HDF5 Group or initialize a new Dataset.
  
      Args:
          bind: The HDF5 group that contains this dataset. If None, a new
              group will be created in memory. Any attributes that already exist
              in ``bind`` will be loaded into this dataset.
          data_name: String describing the type of data this datasets contains, e.g
              'qchem' for quantum chemistry. Defaults to the data name defined by
              the class, this is 'generic' for base datasets.
          identifiers: Tuple of names of attributes of this dataset that will serve
              as its parameters
          **attrs: Attributes to add to this dataset.
      """
      if isinstance(bind, (h5py.Group, h5py.File)):
          self._bind = bind
      else:
          self._bind = hdf5.create_group()
  
      self._init_bind(data_name, identifiers)
  
      for name in self.fields:
          try:
              attr_value = attrs.pop(name)
              setattr(self, name, attr_value)
          except KeyError:
              pass
  
      for name, attr in attrs.items():
          setattr(self, name, attr)
  
imports: import typing
  from dataclasses import dataclass
  from pathlib import Path
  from types import MappingProxyType
  from typing import Any, ClassVar, Generic, List, Literal, Optional, Tuple, Type, TypeVar, Union, cast, get_origin
  from typing_extensions import dataclass_transform
  from pennylane.data.base import hdf5
  from pennylane.data.base.attribute import AttributeInfo, DatasetAttribute
  from pennylane.data.base.hdf5 import HDF5Any, HDF5Group, h5py
  from pennylane.data.base.mapper import MapperMixin, match_obj_type
  from pennylane.data.base.typing_util import UNSET, T
label_desc: Use image node_id 1 to create a new Dataset object from inherited base classes: MapperMixin, _DatasetTransform with example: obj = Dataset(bind)