parameters: (self, source_array)
docstring: Performs a synchronized copy from the `source_array` to the current array.
          This is called through ``x[:] = source_array``, where the `source_array`
          is a `numpy.ndarray` or array-like object.
          This function blocks until all the pending read/write operations with respect
          to the current `NDArray` are finished and carry out the copy operation to the
          current NDArray.
  
          Parameters
          ----------
          source_array : array_like
              The data source we would like to copy from.
  
          Example
          -------
          >>> a = mx.nd.array([1, 2])
          >>> a.asnumpy()
          array([ 1.,  2.], dtype=float32)
          >>> a[:] = np.array([3, 4])
          >> a.asnumpy()
          array([ 3.,  4.], dtype=float32)
docstring_summary: Performs a synchronized copy from the `source_array` to the current array.
          This is called through ``x[:] = source_array``, where the `source_array`
          is a `numpy.ndarray` or array-like object.
          This function blocks until all the pending read/write operations with respect
          to the current `NDArray` are finished and carry out the copy operation to the
          current NDArray.
docstring_tokens: Performs a synchronized copy from the source_array to the current array . This is called through x [ : ] = source_array where the source_array is a numpy . ndarray or array - like object . This function blocks until all the pending read / write operations with respect to the current NDArray are finished and carry out the copy operation to the current NDArray .
function: def _sync_copyfrom(self, source_array):
          """Performs a synchronized copy from the `source_array` to the current array.
          This is called through ``x[:] = source_array``, where the `source_array`
          is a `numpy.ndarray` or array-like object.
          This function blocks until all the pending read/write operations with respect
          to the current `NDArray` are finished and carry out the copy operation to the
          current NDArray.
  
          Parameters
          ----------
          source_array : array_like
              The data source we would like to copy from.
  
          Example
          -------
          >>> a = mx.nd.array([1, 2])
          >>> a.asnumpy()
          array([ 1.,  2.], dtype=float32)
          >>> a[:] = np.array([3, 4])
          >> a.asnumpy()
          array([ 3.,  4.], dtype=float32)
          """
          if not isinstance(source_array, np.ndarray):
              try:
                  source_array = np.array(source_array, dtype=self.dtype)
              except:
                  raise TypeError('array must consist of array-like data,' +
                                  'type %s is not supported' % str(type(array)))
          source_array = np.ascontiguousarray(source_array, dtype=self.dtype)
          if source_array.shape != self.shape:
              raise ValueError('Shape inconsistent: expected %s vs got %s'%(
                  str(self.shape), str(source_array.shape)))
          check_call(_LIB.MXNDArraySyncCopyFromCPU(
              self.handle,
              source_array.ctypes.data_as(ctypes.c_void_p),
              ctypes.c_size_t(source_array.size)))
function_tokens: def _sync_copyfrom ( self , source_array ) : if not isinstance ( source_array , np . ndarray ) : try : source_array = np . array ( source_array , dtype = self . dtype ) except : raise TypeError ( 'array must consist of array-like data,' + 'type %s is not supported' % str ( type ( array ) ) ) source_array = np . ascontiguousarray ( source_array , dtype = self . dtype ) if source_array . shape != self . shape : raise ValueError ( 'Shape inconsistent: expected %s vs got %s' % ( str ( self . shape ) , str ( source_array . shape ) ) ) check_call ( _LIB . MXNDArraySyncCopyFromCPU ( self . handle , source_array . ctypes . data_as ( ctypes . c_void_p ) , ctypes . c_size_t ( source_array . size ) ) )