class_docstr: Stand-in for tf.TensorArray.
code: def __init__(
      self,
      dtype,
      size=None,
      dynamic_size=None,
      clear_after_read=None,
      tensor_array_name=None,
      handle=None,
      flow=None,
      infer_shape=True,
      element_shape=None,
      colocate_with_first_write_call=True,
      data=None,
      name=None,
  ):
      self._dtype = utils.numpy_dtype(dtype)
      if data is None:
          if (
              JAX_MODE
              and size is not None
              and element_shape is not None
          ):
              data = np.empty(
                  (size,) + tuple(element_shape), dtype=self._dtype
              )
          # Can be useful for finding failure cases in JAX TensorArray-using code.
          # elif JAX_MODE:
          #   raise ValueError(
          #       'Missing shape argument: size {} element_shape {}'.format(
          #           size, element_shape))
          else:
              data = [None] * (0 if size is None else int(size))
      self._data = data
      self._size = size
      self._dynamic_size = dynamic_size
      self._clear_after_read = clear_after_read
      self._tensor_array_name = tensor_array_name
      self._handle = handle
      self._flow = flow
      self._infer_shape = infer_shape
      self._element_shape = element_shape
      self._colocate_with_first_write_call = (
          colocate_with_first_write_call
      )
      self._name = name
  
imports: import warnings
  import numpy
  from tensorflow_probability.python.internal.backend.numpy import _utils
  from tensorflow_probability.python.internal.backend.numpy import numpy_array
label_desc: Use image node_id 1 to create a new TensorArray object from inherited base classes: object with example: obj = TensorArray(dtype, size, dynamic_size, clear_after_read, tensor_array_name, handle, flow, infer_shape, element_shape, colocate_with_first_write_call, data, name)