class_docstr: Conditional execution. The return types must be identical between the true
  and false branches.
  
  Parameters
  ----------
  pred: tensor<[], bool> (Required)
      * 0D tensor (scalar) predicate to switch between true and fall branches.
  
  _true_fn: Python function (Required)
      * A Python function that will be executed if ``cond`` evaluates to ``True``. It must take 0 input and return one or more values, whose types will be taken to be the return type of the operation.
  
  _false_fn: Python function (Required)
      * A Python function to be executed if ``cond`` evaluates to ``False``. It must take 0 input and has return types must match those of if_branch.
  
  Returns
  -------
  Python tuple
      * Tuple of ``Variables`` from one of the branches.
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code: def __init__(self, **kwargs):
      super(cond, self).__init__(**kwargs)
  
imports: import six
  from coremltools.converters.mil.mil import types
  from coremltools.converters.mil.mil.types.type_mapping import numpy_val_to_builtin_val, is_subtype
  import copy
  from coremltools.converters.mil.mil import Block, SYMBOL, NONE
  from coremltools.converters.mil.mil.var import Var
  from coremltools.converters.mil.mil import get_new_symbol
  from ._op_reqs import *
  import logging
label_desc: Use image node_id 1 to create a new cond object from inherited base classes: Operation with example: obj = cond()