class_docstr: Initialize a `DiscreteFactor` class.
  
  Defined above, we have the following mapping from variable
  assignments to the index of the row vector in the value field:
  +-----+-----+-----+-------------------+
  |  x1 |  x2 |  x3 |    phi(x1, x2, x3)|
  +-----+-----+-----+-------------------+
  | x1_0| x2_0| x3_0|     phi.value(0)  |
  +-----+-----+-----+-------------------+
  | x1_0| x2_0| x3_1|     phi.value(1)  |
  +-----+-----+-----+-------------------+
  | x1_0| x2_1| x3_0|     phi.value(2)  |
  +-----+-----+-----+-------------------+
  | x1_0| x2_1| x3_1|     phi.value(3)  |
  +-----+-----+-----+-------------------+
  | x1_1| x2_0| x3_0|     phi.value(4)  |
  +-----+-----+-----+-------------------+
  | x1_1| x2_0| x3_1|     phi.value(5)  |
  +-----+-----+-----+-------------------+
  | x1_1| x2_1| x3_0|     phi.value(6)  |
  +-----+-----+-----+-------------------+
  | x1_1| x2_1| x3_1|     phi.value(7)  |
  +-----+-----+-----+-------------------+
  
  Parameters
  ----------
  variables: list, array-like
      List of variables on which the factor is to be defined i.e. scope of the factor.
  
  cardinality: list, array_like
      List of cardinalities/no.of states of each variable. `cardinality`
      array must have a value corresponding to each variable in
      `variables`.
  
  values: list, array_like
      List of values of factor.
      A DiscreteFactor's values are stored in a row vector in the value
      using an ordering such that the left-most variables as defined in
      `variables` cycle through their values the fastest. Please refer
      to examples for usage examples.
  
  Examples
  --------
  >>> import numpy as np
  >>> from pgmpy.factors.discrete import DiscreteFactor
  >>> phi = DiscreteFactor(['x1', 'x2', 'x3'], [2, 2, 2], np.ones(8))
  >>> phi
  <DiscreteFactor representing phi(x1:2, x2:2, x3:2) at 0x7f8188fcaa90>
  >>> print(phi)
  +------+------+------+-----------------+
  | x1   | x2   | x3   |   phi(x1,x2,x3) |
  |------+------+------+-----------------|
  | x1_0 | x2_0 | x3_0 |          1.0000 |
  | x1_0 | x2_0 | x3_1 |          1.0000 |
  | x1_0 | x2_1 | x3_0 |          1.0000 |
  | x1_0 | x2_1 | x3_1 |          1.0000 |
  | x1_1 | x2_0 | x3_0 |          1.0000 |
  | x1_1 | x2_0 | x3_1 |          1.0000 |
  | x1_1 | x2_1 | x3_0 |          1.0000 |
  | x1_1 | x2_1 | x3_1 |          1.0000 |
  +------+------+------+-----------------+
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docstr: Returns a list of assignments (variable and state) for the corresponding index.
  
  Parameters
  ----------
  index: list, array-like
      List of indices whose assignment is to be computed
  
  Returns
  -------
  Full assignments: list
      Returns a list of full assignments of all the variables of the factor.
  
  Examples
  --------
  >>> import numpy as np
  >>> from pgmpy.factors.discrete import DiscreteFactor
  >>> phi = DiscreteFactor(['diff', 'intel'], [2, 2], np.ones(4))
  >>> phi.assignment([1, 2])
  [[('diff', 0), ('intel', 1)], [('diff', 1), ('intel', 0)]]
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code: def assignment(self, index):
      """
      Returns a list of assignments (variable and state) for the corresponding index.
  
      Parameters
      ----------
      index: list, array-like
          List of indices whose assignment is to be computed
  
      Returns
      -------
      Full assignments: list
          Returns a list of full assignments of all the variables of the factor.
  
      Examples
      --------
      >>> import numpy as np
      >>> from pgmpy.factors.discrete import DiscreteFactor
      >>> phi = DiscreteFactor(['diff', 'intel'], [2, 2], np.ones(4))
      >>> phi.assignment([1, 2])
      [[('diff', 0), ('intel', 1)], [('diff', 1), ('intel', 0)]]
      """
      if config.get_backend() == "numpy":
          index = np.array(index)
      else:
          if (len(index) == 1) and (isinstance(index[0], torch.Tensor)):
              index = index[0][None]
          else:
              index = torch.tensor(
                  index, dtype=torch.int, device=config.get_device()
              )
  
      max_possible_index = np.prod(self.cardinality) - 1
      if not all(i <= max_possible_index for i in index):
          raise IndexError("Index greater than max possible index")
  
      assignments = compat_fns.get_compute_backend().zeros(
          (len(index), len(self.scope())), dtype=int
      )
      rev_card = self.cardinality[::-1]
      for i, card in enumerate(rev_card):
          assignments[:, i] = index % card
          index = index // card
  
      assignments = compat_fns.flip(assignments, axis=(1,))
  
      return [
          [
              (key, self.get_state_names(key, int(val)))
              for key, val in zip(self.variables, values)
          ]
          for values in assignments
      ]
  
imports: from collections import namedtuple
  from itertools import product
  import numpy
  import pandas
  import torch
  from pgmpy import config
  from pgmpy.extern import tabulate
  from pgmpy.factors.base import BaseFactor
  from pgmpy.global_vars import logger
  from pgmpy.utils import StateNameMixin, compat_fns
label_desc: Use image node_id 6 for calling the DiscreteFactor obj's underlying member method code with example usage: obj.assignment(index) and returns: unknown