parameters: (self, key, out=None, priority=0, ignore_sparse=True)
docstring: Pulls a single value or a sequence of values from the store.
  
          This function returns immediately after adding an operator to the engine.
          Subsequent attempts to read from the `out` variable will be blocked until the
          pull operation completes.
  
          `pull` is executed asynchronously after all previous `pull` calls and only
          the last `push` call for the same input key(s) are finished.
  
          The returned values are guaranteed to be the latest values in the store.
  
          pull with `RowSparseNDArray` is not supported for dist kvstore.
          Please use ``row_sparse_pull`` instead.
  
          Parameters
          ----------
          key : str, int, or sequence of str or int
              Keys.
  
          out: NDArray or list of NDArray or list of list of NDArray
              Values corresponding to the keys.
  
          priority : int, optional
              The priority of the pull operation.
              Higher priority pull operations are likely to be executed before
              other pull actions.
  
          ignore_sparse: bool, optional, default True
              Whether to ignore sparse arrays in the request.
  
          Examples
          --------
          >>> # pull a single key-value pair
          >>> a = mx.nd.zeros(shape)
          >>> kv.pull('3', out=a)
          >>> print a.asnumpy()
          [[ 2.  2.  2.]
          [ 2.  2.  2.]]
  
          >>> # pull into multiple devices
          >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
          >>> kv.pull('3', out=b)
          >>> print b[1].asnumpy()
          [[ 2.  2.  2.]
          [ 2.  2.  2.]]
  
          >>> # pull a list of key-value pairs.
          >>> # On single device
          >>> keys = ['5', '7', '9']
          >>> b = [mx.nd.zeros(shape)]*len(keys)
          >>> kv.pull(keys, out=b)
          >>> print b[1].asnumpy()
          [[ 2.  2.  2.]
          [ 2.  2.  2.]]
          >>> # On multiple devices
          >>> keys = ['6', '8', '10']
          >>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
          >>> kv.pull(keys, out=b)
          >>> print b[1][1].asnumpy()
          [[ 2.  2.  2.]
          [ 2.  2.  2.]]
docstring_summary: Pulls a single value or a sequence of values from the store.
docstring_tokens: Pulls a single value or a sequence of values from the store .
function: def pull(self, key, out=None, priority=0, ignore_sparse=True):
          """ Pulls a single value or a sequence of values from the store.
  
          This function returns immediately after adding an operator to the engine.
          Subsequent attempts to read from the `out` variable will be blocked until the
          pull operation completes.
  
          `pull` is executed asynchronously after all previous `pull` calls and only
          the last `push` call for the same input key(s) are finished.
  
          The returned values are guaranteed to be the latest values in the store.
  
          pull with `RowSparseNDArray` is not supported for dist kvstore.
          Please use ``row_sparse_pull`` instead.
  
          Parameters
          ----------
          key : str, int, or sequence of str or int
              Keys.
  
          out: NDArray or list of NDArray or list of list of NDArray
              Values corresponding to the keys.
  
          priority : int, optional
              The priority of the pull operation.
              Higher priority pull operations are likely to be executed before
              other pull actions.
  
          ignore_sparse: bool, optional, default True
              Whether to ignore sparse arrays in the request.
  
          Examples
          --------
          >>> # pull a single key-value pair
          >>> a = mx.nd.zeros(shape)
          >>> kv.pull('3', out=a)
          >>> print a.asnumpy()
          [[ 2.  2.  2.]
          [ 2.  2.  2.]]
  
          >>> # pull into multiple devices
          >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
          >>> kv.pull('3', out=b)
          >>> print b[1].asnumpy()
          [[ 2.  2.  2.]
          [ 2.  2.  2.]]
  
          >>> # pull a list of key-value pairs.
          >>> # On single device
          >>> keys = ['5', '7', '9']
          >>> b = [mx.nd.zeros(shape)]*len(keys)
          >>> kv.pull(keys, out=b)
          >>> print b[1].asnumpy()
          [[ 2.  2.  2.]
          [ 2.  2.  2.]]
          >>> # On multiple devices
          >>> keys = ['6', '8', '10']
          >>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
          >>> kv.pull(keys, out=b)
          >>> print b[1][1].asnumpy()
          [[ 2.  2.  2.]
          [ 2.  2.  2.]]
          """
          assert(out is not None)
          ckeys, cvals, use_str_keys = _ctype_key_value(key, out)
          if use_str_keys:
              check_call(_LIB.MXKVStorePullWithSparseEx(self.handle, mx_uint(len(ckeys)), ckeys,
                                                        cvals, ctypes.c_int(priority),
                                                        ctypes.c_bool(ignore_sparse)))
          else:
              check_call(_LIB.MXKVStorePullWithSparse(self.handle, mx_uint(len(ckeys)), ckeys,
                                                      cvals, ctypes.c_int(priority),
                                                      ctypes.c_bool(ignore_sparse)))
function_tokens: def pull ( self , key , out = None , priority = 0 , ignore_sparse = True ) : assert ( out is not None ) ckeys , cvals , use_str_keys = _ctype_key_value ( key , out ) if use_str_keys : check_call ( _LIB . MXKVStorePullWithSparseEx ( self . handle , mx_uint ( len ( ckeys ) ) , ckeys , cvals , ctypes . c_int ( priority ) , ctypes . c_bool ( ignore_sparse ) ) ) else : check_call ( _LIB . MXKVStorePullWithSparse ( self . handle , mx_uint ( len ( ckeys ) ) , ckeys , cvals , ctypes . c_int ( priority ) , ctypes . c_bool ( ignore_sparse ) ) )