(shape, dtype=float)
return arrays_lib.tensor_to_ndarray(tf.zeros(shape, dtype=dtype))
Returns an ndarray with the given shape and type filled with zeros.

  Args:
    shape: A fully defined shape. Could be - NumPy array or a python scalar,
      list or tuple of integers, - TensorFlow tensor/ndarray of integer type and
      rank <=1.
    dtype: Optional, defaults to float. The type of the resulting ndarray. Could
      be a python type, a NumPy type or a TensorFlow `DType`.

  Returns:
    An ndarray.
Returns an ndarray with the given shape and type filled with zeros.
Returns an ndarray with the given shape and type filled with zeros .
def zeros(shape, dtype=float):  # pylint: disable=redefined-outer-name
  """Returns an ndarray with the given shape and type filled with zeros.

  Args:
    shape: A fully defined shape. Could be - NumPy array or a python scalar,
      list or tuple of integers, - TensorFlow tensor/ndarray of integer type and
      rank <=1.
    dtype: Optional, defaults to float. The type of the resulting ndarray. Could
      be a python type, a NumPy type or a TensorFlow `DType`.

  Returns:
    An ndarray.
  """
  if dtype:
    dtype = utils.result_type(dtype)
  if isinstance(shape, arrays_lib.ndarray):
    shape = shape.data
  return arrays_lib.tensor_to_ndarray(tf.zeros(shape, dtype=dtype))
def zeros ( shape , dtype = float ) : # pylint: disable=redefined-outer-name if dtype : dtype = utils . result_type ( dtype ) if isinstance ( shape , arrays_lib . ndarray ) : shape = shape . data return arrays_lib . tensor_to_ndarray ( tf . zeros ( shape , dtype = dtype ) )