text: """Python wrappers around TensorFlow ops.
  
  This file is MACHINE GENERATED! Do not edit.
  Original C++ source file: lstm_ops.cc
  """
  
  import collections as _collections
  import six as _six
  
  from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
  from tensorflow.python.eager import context as _context
  from tensorflow.python.eager import core as _core
  from tensorflow.python.eager import execute as _execute
  from tensorflow.python.framework import dtypes as _dtypes
  from tensorflow.python.framework import errors as _errors
  from tensorflow.python.framework import tensor_shape as _tensor_shape
  
  from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
  # Needed to trigger the call to _set_call_cpp_shape_fn.
  from tensorflow.python.framework import common_shapes as _common_shapes
  from tensorflow.python.framework import op_def_registry as _op_def_registry
  from tensorflow.python.framework import ops as _ops
  from tensorflow.python.framework import op_def_library as _op_def_library
  from tensorflow.python.util.tf_export import tf_export
  
  
  _block_lstm_outputs = ["i", "cs", "f", "o", "ci", "co", "h"]
  _BlockLSTMOutput = _collections.namedtuple(
      "BlockLSTM", _block_lstm_outputs)
  
  
  @tf_export('block_lstm')
  def block_lstm(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None):
    r"""Computes the LSTM cell forward propagation for all the time steps.
  
    This is equivalent to applying LSTMBlockCell in a loop, like so:
  
    ```python
    for x1 in unpack(x):
      i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
        x1, cs_prev, h_prev, w, wci, wcf, wco, b)
      cs_prev = cs1
      h_prev = h1
      i.append(i1)
      cs.append(cs1)
      f.append(f1)
      o.append(o1)
      ci.append(ci1)
      co.append(co1)
      h.append(h1)
    return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h)
    ```
  
    Args:
      seq_len_max: A `Tensor` of type `int64`.
        Maximum time length actually used by this input. Outputs are padded
        with zeros beyond this length.
      x: A `Tensor`. Must be one of the following types: `float32`.
        The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
      cs_prev: A `Tensor`. Must have the same type as `x`.
        Value of the initial cell state.
      h_prev: A `Tensor`. Must have the same type as `x`.
        Initial output of cell (to be used for peephole).
      w: A `Tensor`. Must have the same type as `x`. The weight matrix.
      wci: A `Tensor`. Must have the same type as `x`.
        The weight matrix for input gate peephole connection.
      wcf: A `Tensor`. Must have the same type as `x`.
        The weight matrix for forget gate peephole connection.
      wco: A `Tensor`. Must have the same type as `x`.
        The weight matrix for output gate peephole connection.
      b: A `Tensor`. Must have the same type as `x`. The bias vector.
      forget_bias: An optional `float`. Defaults to `1`. The forget gate bias.
      cell_clip: An optional `float`. Defaults to `3`.
        Value to clip the 'cs' value to.
      use_peephole: An optional `bool`. Defaults to `False`.
        Whether to use peephole weights.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (i, cs, f, o, ci, co, h).
  
      i: A `Tensor`. Has the same type as `x`. The input gate over the whole time sequence.
      cs: A `Tensor`. Has the same type as `x`. The cell state before the tanh over the whole time sequence.
      f: A `Tensor`. Has the same type as `x`. The forget gate over the whole time sequence.
      o: A `Tensor`. Has the same type as `x`. The output gate over the whole time sequence.
      ci: A `Tensor`. Has the same type as `x`. The cell input over the whole time sequence.
      co: A `Tensor`. Has the same type as `x`. The cell after the tanh over the whole time sequence.
      h: A `Tensor`. Has the same type as `x`. The output h vector over the whole time sequence.
    """
    _ctx = _context._context
    if _ctx is None or not _ctx._eager_context.is_eager:
      if forget_bias is None:
        forget_bias = 1
      forget_bias = _execute.make_float(forget_bias, "forget_bias")
      if cell_clip is None:
        cell_clip = 3
      cell_clip = _execute.make_float(cell_clip, "cell_clip")
      if use_peephole is None:
        use_peephole = False
      use_peephole = _execute.make_bool(use_peephole, "use_peephole")
      _, _, _op = _op_def_lib._apply_op_helper(
          "BlockLSTM", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev,
          h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b,
          forget_bias=forget_bias, cell_clip=cell_clip,
          use_peephole=use_peephole, name=name)
      _result = _op.outputs[:]
      _inputs_flat = _op.inputs
      _attrs = ("forget_bias", _op.get_attr("forget_bias"), "cell_clip",
                _op.get_attr("cell_clip"), "use_peephole",
                _op.get_attr("use_peephole"), "T", _op.get_attr("T"))
      _execute.record_gradient(
        "BlockLSTM", _inputs_flat, _attrs, _result, name)
      _result = _BlockLSTMOutput._make(_result)
      return _result
  
    else:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._eager_context.device_name, "BlockLSTM",
          name, _ctx._post_execution_callbacks, seq_len_max, x, cs_prev, h_prev,
          w, wci, wcf, wco, b, "forget_bias", forget_bias, "cell_clip",
          cell_clip, "use_peephole", use_peephole)
        _result = _BlockLSTMOutput._make(_result)
        return _result
      except _core._FallbackException:
        return block_lstm_eager_fallback(
            seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b,
            forget_bias=forget_bias, cell_clip=cell_clip,
            use_peephole=use_peephole, name=name, ctx=_ctx)
      except _core._NotOkStatusException as e:
        if name is not None:
          message = e.message + " name: " + name
        else:
          message = e.message
        _six.raise_from(_core._status_to_exception(e.code, message), None)
  
  
  def block_lstm_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function block_lstm
    """
    _ctx = ctx if ctx else _context.context()
    if forget_bias is None:
      forget_bias = 1
    forget_bias = _execute.make_float(forget_bias, "forget_bias")
    if cell_clip is None:
      cell_clip = 3
    cell_clip = _execute.make_float(cell_clip, "cell_clip")
    if use_peephole is None:
      use_peephole = False
    use_peephole = _execute.make_bool(use_peephole, "use_peephole")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], _ctx)
    (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T
    seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64)
    _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b]
    _attrs = ("forget_bias", forget_bias, "cell_clip", cell_clip,
    "use_peephole", use_peephole, "T", _attr_T)
    _result = _execute.execute(b"BlockLSTM", 7, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "BlockLSTM", _inputs_flat, _attrs, _result, name)
    _result = _BlockLSTMOutput._make(_result)
    return _result
  
  _ops.RegisterShape("BlockLSTM")(None)
  
  
  _block_lstm_grad_outputs = ["x_grad", "cs_prev_grad", "h_prev_grad", "w_grad",
                             "wci_grad", "wcf_grad", "wco_grad", "b_grad"]
  _BlockLSTMGradOutput = _collections.namedtuple(
      "BlockLSTMGrad", _block_lstm_grad_outputs)
  
  
  @tf_export('block_lstm_grad')
  def block_lstm_grad(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None):
    r"""Computes the LSTM cell backward propagation for the entire time sequence.
  
    This implementation is to be used in conjunction of LSTMBlock.
  
    Args:
      seq_len_max: A `Tensor` of type `int64`.
        Maximum time length actually used by this input. Outputs are padded
        with zeros beyond this length.
      x: A `Tensor`. Must be one of the following types: `float32`.
        The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
      cs_prev: A `Tensor`. Must have the same type as `x`.
        Value of the initial cell state.
      h_prev: A `Tensor`. Must have the same type as `x`.
        Initial output of cell (to be used for peephole).
      w: A `Tensor`. Must have the same type as `x`. The weight matrix.
      wci: A `Tensor`. Must have the same type as `x`.
        The weight matrix for input gate peephole connection.
      wcf: A `Tensor`. Must have the same type as `x`.
        The weight matrix for forget gate peephole connection.
      wco: A `Tensor`. Must have the same type as `x`.
        The weight matrix for output gate peephole connection.
      b: A `Tensor`. Must have the same type as `x`. The bias vector.
      i: A `Tensor`. Must have the same type as `x`.
        The input gate over the whole time sequence.
      cs: A `Tensor`. Must have the same type as `x`.
        The cell state before the tanh over the whole time sequence.
      f: A `Tensor`. Must have the same type as `x`.
        The forget gate over the whole time sequence.
      o: A `Tensor`. Must have the same type as `x`.
        The output gate over the whole time sequence.
      ci: A `Tensor`. Must have the same type as `x`.
        The cell input over the whole time sequence.
      co: A `Tensor`. Must have the same type as `x`.
        The cell after the tanh over the whole time sequence.
      h: A `Tensor`. Must have the same type as `x`.
        The output h vector over the whole time sequence.
      cs_grad: A `Tensor`. Must have the same type as `x`.
        The current gradient of cs.
      h_grad: A `Tensor`. Must have the same type as `x`.
        The gradient of h vector.
      use_peephole: A `bool`. Whether to use peephole weights.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad).
  
      x_grad: A `Tensor`. Has the same type as `x`. The gradient of x to be back-propped.
      cs_prev_grad: A `Tensor`. Has the same type as `x`. The gradient of cs_prev to be back-propped.
      h_prev_grad: A `Tensor`. Has the same type as `x`. The gradient of h_prev to be back-propped.
      w_grad: A `Tensor`. Has the same type as `x`. The gradient for w to be back-propped.
      wci_grad: A `Tensor`. Has the same type as `x`. The gradient for wci to be back-propped.
      wcf_grad: A `Tensor`. Has the same type as `x`. The gradient for wcf to be back-propped.
      wco_grad: A `Tensor`. Has the same type as `x`. The gradient for wco to be back-propped.
      b_grad: A `Tensor`. Has the same type as `x`. The gradient for w to be back-propped.
    """
    _ctx = _context._context
    if _ctx is None or not _ctx._eager_context.is_eager:
      use_peephole = _execute.make_bool(use_peephole, "use_peephole")
      _, _, _op = _op_def_lib._apply_op_helper(
          "BlockLSTMGrad", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev,
          h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f,
          o=o, ci=ci, co=co, h=h, cs_grad=cs_grad, h_grad=h_grad,
          use_peephole=use_peephole, name=name)
      _result = _op.outputs[:]
      _inputs_flat = _op.inputs
      _attrs = ("use_peephole", _op.get_attr("use_peephole"), "T",
                _op.get_attr("T"))
      _execute.record_gradient(
        "BlockLSTMGrad", _inputs_flat, _attrs, _result, name)
      _result = _BlockLSTMGradOutput._make(_result)
      return _result
  
    else:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._eager_context.device_name,
          "BlockLSTMGrad", name, _ctx._post_execution_callbacks, seq_len_max, x,
          cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad,
          h_grad, "use_peephole", use_peephole)
        _result = _BlockLSTMGradOutput._make(_result)
        return _result
      except _core._FallbackException:
        return block_lstm_grad_eager_fallback(
            seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o,
            ci, co, h, cs_grad, h_grad, use_peephole=use_peephole, name=name,
            ctx=_ctx)
      except _core._NotOkStatusException as e:
        if name is not None:
          message = e.message + " name: " + name
        else:
          message = e.message
        _six.raise_from(_core._status_to_exception(e.code, message), None)
  
  
  def block_lstm_grad_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function block_lstm_grad
    """
    _ctx = ctx if ctx else _context.context()
    use_peephole = _execute.make_bool(use_peephole, "use_peephole")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad], _ctx)
    (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad) = _inputs_T
    seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64)
    _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad]
    _attrs = ("use_peephole", use_peephole, "T", _attr_T)
    _result = _execute.execute(b"BlockLSTMGrad", 8, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "BlockLSTMGrad", _inputs_flat, _attrs, _result, name)
    _result = _BlockLSTMGradOutput._make(_result)
    return _result
  
  _ops.RegisterShape("BlockLSTMGrad")(None)
  
  
  _lstm_block_cell_outputs = ["i", "cs", "f", "o", "ci", "co", "h"]
  _LSTMBlockCellOutput = _collections.namedtuple(
      "LSTMBlockCell", _lstm_block_cell_outputs)
  
  
  @tf_export('lstm_block_cell')
  def lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None):
    r"""Computes the LSTM cell forward propagation for 1 time step.
  
    This implementation uses 1 weight matrix and 1 bias vector, and there's an
    optional peephole connection.
  
    This kernel op implements the following mathematical equations:
  
    ```python
    xh = [x, h_prev]
    [i, f, ci, o] = xh * w + b
    f = f + forget_bias
  
    if not use_peephole:
      wci = wcf = wco = 0
  
    i = sigmoid(cs_prev * wci + i)
    f = sigmoid(cs_prev * wcf + f)
    ci = tanh(ci)
  
    cs = ci .* i + cs_prev .* f
    cs = clip(cs, cell_clip)
  
    o = sigmoid(cs * wco + o)
    co = tanh(cs)
    h = co .* o
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`.
        The input to the LSTM cell, shape (batch_size, num_inputs).
      cs_prev: A `Tensor`. Must have the same type as `x`.
        Value of the cell state at previous time step.
      h_prev: A `Tensor`. Must have the same type as `x`.
        Output of the previous cell at previous time step.
      w: A `Tensor`. Must have the same type as `x`. The weight matrix.
      wci: A `Tensor`. Must have the same type as `x`.
        The weight matrix for input gate peephole connection.
      wcf: A `Tensor`. Must have the same type as `x`.
        The weight matrix for forget gate peephole connection.
      wco: A `Tensor`. Must have the same type as `x`.
        The weight matrix for output gate peephole connection.
      b: A `Tensor`. Must have the same type as `x`. The bias vector.
      forget_bias: An optional `float`. Defaults to `1`. The forget gate bias.
      cell_clip: An optional `float`. Defaults to `3`.
        Value to clip the 'cs' value to.
      use_peephole: An optional `bool`. Defaults to `False`.
        Whether to use peephole weights.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (i, cs, f, o, ci, co, h).
  
      i: A `Tensor`. Has the same type as `x`. The input gate.
      cs: A `Tensor`. Has the same type as `x`. The cell state before the tanh.
      f: A `Tensor`. Has the same type as `x`. The forget gate.
      o: A `Tensor`. Has the same type as `x`. The output gate.
      ci: A `Tensor`. Has the same type as `x`. The cell input.
      co: A `Tensor`. Has the same type as `x`. The cell after the tanh.
      h: A `Tensor`. Has the same type as `x`. The output h vector.
    """
    _ctx = _context._context
    if _ctx is None or not _ctx._eager_context.is_eager:
      if forget_bias is None:
        forget_bias = 1
      forget_bias = _execute.make_float(forget_bias, "forget_bias")
      if cell_clip is None:
        cell_clip = 3
      cell_clip = _execute.make_float(cell_clip, "cell_clip")
      if use_peephole is None:
        use_peephole = False
      use_peephole = _execute.make_bool(use_peephole, "use_peephole")
      _, _, _op = _op_def_lib._apply_op_helper(
          "LSTMBlockCell", x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci,
          wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip,
          use_peephole=use_peephole, name=name)
      _result = _op.outputs[:]
      _inputs_flat = _op.inputs
      _attrs = ("forget_bias", _op.get_attr("forget_bias"), "cell_clip",
                _op.get_attr("cell_clip"), "use_peephole",
                _op.get_attr("use_peephole"), "T", _op.get_attr("T"))
      _execute.record_gradient(
        "LSTMBlockCell", _inputs_flat, _attrs, _result, name)
      _result = _LSTMBlockCellOutput._make(_result)
      return _result
  
    else:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._eager_context.device_name,
          "LSTMBlockCell", name, _ctx._post_execution_callbacks, x, cs_prev,
          h_prev, w, wci, wcf, wco, b, "forget_bias", forget_bias, "cell_clip",
          cell_clip, "use_peephole", use_peephole)
        _result = _LSTMBlockCellOutput._make(_result)
        return _result
      except _core._FallbackException:
        return lstm_block_cell_eager_fallback(
            x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=forget_bias,
            cell_clip=cell_clip, use_peephole=use_peephole, name=name, ctx=_ctx)
      except _core._NotOkStatusException as e:
        if name is not None:
          message = e.message + " name: " + name
        else:
          message = e.message
        _six.raise_from(_core._status_to_exception(e.code, message), None)
  
  
  def lstm_block_cell_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function lstm_block_cell
    """
    _ctx = ctx if ctx else _context.context()
    if forget_bias is None:
      forget_bias = 1
    forget_bias = _execute.make_float(forget_bias, "forget_bias")
    if cell_clip is None:
      cell_clip = 3
    cell_clip = _execute.make_float(cell_clip, "cell_clip")
    if use_peephole is None:
      use_peephole = False
    use_peephole = _execute.make_bool(use_peephole, "use_peephole")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], _ctx)
    (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T
    _inputs_flat = [x, cs_prev, h_prev, w, wci, wcf, wco, b]
    _attrs = ("forget_bias", forget_bias, "cell_clip", cell_clip,
    "use_peephole", use_peephole, "T", _attr_T)
    _result = _execute.execute(b"LSTMBlockCell", 7, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "LSTMBlockCell", _inputs_flat, _attrs, _result, name)
    _result = _LSTMBlockCellOutput._make(_result)
    return _result
  
  _ops.RegisterShape("LSTMBlockCell")(None)
  
  
  _lstm_block_cell_grad_outputs = ["cs_prev_grad", "dicfo", "wci_grad",
                                  "wcf_grad", "wco_grad"]
  _LSTMBlockCellGradOutput = _collections.namedtuple(
      "LSTMBlockCellGrad", _lstm_block_cell_grad_outputs)
  
  
  @tf_export('lstm_block_cell_grad')
  def lstm_block_cell_grad(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None):
    r"""Computes the LSTM cell backward propagation for 1 timestep.
  
    This implementation is to be used in conjunction of LSTMBlockCell.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`.
        The input to the LSTM cell, shape (batch_size, num_inputs).
      cs_prev: A `Tensor`. Must have the same type as `x`.
        The previous cell state.
      h_prev: A `Tensor`. Must have the same type as `x`. The previous h state.
      w: A `Tensor`. Must have the same type as `x`. The weight matrix.
      wci: A `Tensor`. Must have the same type as `x`.
        The weight matrix for input gate peephole connection.
      wcf: A `Tensor`. Must have the same type as `x`.
        The weight matrix for forget gate peephole connection.
      wco: A `Tensor`. Must have the same type as `x`.
        The weight matrix for output gate peephole connection.
      b: A `Tensor`. Must have the same type as `x`. The bias vector.
      i: A `Tensor`. Must have the same type as `x`. The input gate.
      cs: A `Tensor`. Must have the same type as `x`.
        The cell state before the tanh.
      f: A `Tensor`. Must have the same type as `x`. The forget gate.
      o: A `Tensor`. Must have the same type as `x`. The output gate.
      ci: A `Tensor`. Must have the same type as `x`. The cell input.
      co: A `Tensor`. Must have the same type as `x`. The cell after the tanh.
      cs_grad: A `Tensor`. Must have the same type as `x`.
        The current gradient of cs.
      h_grad: A `Tensor`. Must have the same type as `x`.
        The gradient of h vector.
      use_peephole: A `bool`. Whether the cell uses peephole connections.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (cs_prev_grad, dicfo, wci_grad, wcf_grad, wco_grad).
  
      cs_prev_grad: A `Tensor`. Has the same type as `x`. The gradient of cs to be back-propped.
      dicfo: A `Tensor`. Has the same type as `x`. The derivative wrt to [i, cs, f, o].
      wci_grad: A `Tensor`. Has the same type as `x`. The gradient for wci to be back-propped.
      wcf_grad: A `Tensor`. Has the same type as `x`. The gradient for wcf to be back-propped.
      wco_grad: A `Tensor`. Has the same type as `x`. The gradient for wco to be back-propped.
    """
    _ctx = _context._context
    if _ctx is None or not _ctx._eager_context.is_eager:
      use_peephole = _execute.make_bool(use_peephole, "use_peephole")
      _, _, _op = _op_def_lib._apply_op_helper(
          "LSTMBlockCellGrad", x=x, cs_prev=cs_prev, h_prev=h_prev, w=w,
          wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f, o=o, ci=ci, co=co,
          cs_grad=cs_grad, h_grad=h_grad, use_peephole=use_peephole, name=name)
      _result = _op.outputs[:]
      _inputs_flat = _op.inputs
      _attrs = ("use_peephole", _op.get_attr("use_peephole"), "T",
                _op.get_attr("T"))
      _execute.record_gradient(
        "LSTMBlockCellGrad", _inputs_flat, _attrs, _result, name)
      _result = _LSTMBlockCellGradOutput._make(_result)
      return _result
  
    else:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._eager_context.device_name,
          "LSTMBlockCellGrad", name, _ctx._post_execution_callbacks, x, cs_prev,
          h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad,
          "use_peephole", use_peephole)
        _result = _LSTMBlockCellGradOutput._make(_result)
        return _result
      except _core._FallbackException:
        return lstm_block_cell_grad_eager_fallback(
            x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co,
            cs_grad, h_grad, use_peephole=use_peephole, name=name, ctx=_ctx)
      except _core._NotOkStatusException as e:
        if name is not None:
          message = e.message + " name: " + name
        else:
          message = e.message
        _six.raise_from(_core._status_to_exception(e.code, message), None)
  
  
  def lstm_block_cell_grad_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function lstm_block_cell_grad
    """
    _ctx = ctx if ctx else _context.context()
    use_peephole = _execute.make_bool(use_peephole, "use_peephole")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad], _ctx)
    (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad) = _inputs_T
    _inputs_flat = [x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad]
    _attrs = ("use_peephole", use_peephole, "T", _attr_T)
    _result = _execute.execute(b"LSTMBlockCellGrad", 5, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "LSTMBlockCellGrad", _inputs_flat, _attrs, _result, name)
    _result = _LSTMBlockCellGradOutput._make(_result)
    return _result
  
  _ops.RegisterShape("LSTMBlockCellGrad")(None)
  
  def _InitOpDefLibrary(op_list_proto_bytes):
    op_list = _op_def_pb2.OpList()
    op_list.ParseFromString(op_list_proto_bytes)
    _op_def_registry.register_op_list(op_list)
    op_def_lib = _op_def_library.OpDefLibrary()
    op_def_lib.add_op_list(op_list)
    return op_def_lib
  # op {
  #   name: "BlockLSTM"
  #   input_arg {
  #     name: "seq_len_max"
  #     type: DT_INT64
  #   }
  #   input_arg {
  #     name: "x"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "cs_prev"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "h_prev"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "w"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wci"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wcf"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wco"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "b"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "i"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "cs"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "f"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "o"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "ci"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "co"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "h"
  #     type_attr: "T"
  #   }
  #   attr {
  #     name: "forget_bias"
  #     type: "float"
  #     default_value {
  #       f: 1
  #     }
  #   }
  #   attr {
  #     name: "cell_clip"
  #     type: "float"
  #     default_value {
  #       f: 3
  #     }
  #   }
  #   attr {
  #     name: "use_peephole"
  #     type: "bool"
  #     default_value {
  #       b: false
  #     }
  #   }
  #   attr {
  #     name: "T"
  #     type: "type"
  #     allowed_values {
  #       list {
  #         type: DT_FLOAT
  #       }
  #     }
  #   }
  # }
  # op {
  #   name: "BlockLSTMGrad"
  #   input_arg {
  #     name: "seq_len_max"
  #     type: DT_INT64
  #   }
  #   input_arg {
  #     name: "x"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "cs_prev"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "h_prev"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "w"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wci"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wcf"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wco"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "b"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "i"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "cs"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "f"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "o"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "ci"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "co"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "h"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "cs_grad"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "h_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "x_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "cs_prev_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "h_prev_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "w_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "wci_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "wcf_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "wco_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "b_grad"
  #     type_attr: "T"
  #   }
  #   attr {
  #     name: "use_peephole"
  #     type: "bool"
  #   }
  #   attr {
  #     name: "T"
  #     type: "type"
  #     allowed_values {
  #       list {
  #         type: DT_FLOAT
  #       }
  #     }
  #   }
  # }
  # op {
  #   name: "LSTMBlockCell"
  #   input_arg {
  #     name: "x"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "cs_prev"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "h_prev"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "w"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wci"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wcf"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wco"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "b"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "i"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "cs"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "f"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "o"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "ci"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "co"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "h"
  #     type_attr: "T"
  #   }
  #   attr {
  #     name: "forget_bias"
  #     type: "float"
  #     default_value {
  #       f: 1
  #     }
  #   }
  #   attr {
  #     name: "cell_clip"
  #     type: "float"
  #     default_value {
  #       f: 3
  #     }
  #   }
  #   attr {
  #     name: "use_peephole"
  #     type: "bool"
  #     default_value {
  #       b: false
  #     }
  #   }
  #   attr {
  #     name: "T"
  #     type: "type"
  #     allowed_values {
  #       list {
  #         type: DT_FLOAT
  #       }
  #     }
  #   }
  # }
  # op {
  #   name: "LSTMBlockCellGrad"
  #   input_arg {
  #     name: "x"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "cs_prev"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "h_prev"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "w"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wci"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wcf"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "wco"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "b"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "i"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "cs"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "f"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "o"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "ci"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "co"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "cs_grad"
  #     type_attr: "T"
  #   }
  #   input_arg {
  #     name: "h_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "cs_prev_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "dicfo"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "wci_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "wcf_grad"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "wco_grad"
  #     type_attr: "T"
  #   }
  #   attr {
  #     name: "use_peephole"
  #     type: "bool"
  #   }
  #   attr {
  #     name: "T"
  #     type: "type"
  #     allowed_values {
  #       list {
  #         type: DT_FLOAT
  #       }
  #     }
  #   }
  # }
  _op_def_lib = _InitOpDefLibrary(b"\n\214\002\n\tBlockLSTM\022\017\n\013seq_len_max\030\t\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\032\006\n\001i\"\001T\032\007\n\002cs\"\001T\032\006\n\001f\"\001T\032\006\n\001o\"\001T\032\007\n\002ci\"\001T\032\007\n\002co\"\001T\032\006\n\001h\"\001T\"\033\n\013forget_bias\022\005float\032\005%\000\000\200?\"\031\n\tcell_clip\022\005float\032\005%\000\000@@\"\030\n\014use_peephole\022\004bool\032\002(\000\"\020\n\001T\022\004type:\005\n\0032\001\001\n\350\002\n\rBlockLSTMGrad\022\017\n\013seq_len_max\030\t\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\022\006\n\001i\"\001T\022\007\n\002cs\"\001T\022\006\n\001f\"\001T\022\006\n\001o\"\001T\022\007\n\002ci\"\001T\022\007\n\002co\"\001T\022\006\n\001h\"\001T\022\014\n\007cs_grad\"\001T\022\013\n\006h_grad\"\001T\032\013\n\006x_grad\"\001T\032\021\n\014cs_prev_grad\"\001T\032\020\n\013h_prev_grad\"\001T\032\013\n\006w_grad\"\001T\032\r\n\010wci_grad\"\001T\032\r\n\010wcf_grad\"\001T\032\r\n\010wco_grad\"\001T\032\013\n\006b_grad\"\001T\"\024\n\014use_peephole\022\004bool\"\020\n\001T\022\004type:\005\n\0032\001\001\n\377\001\n\rLSTMBlockCell\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\032\006\n\001i\"\001T\032\007\n\002cs\"\001T\032\006\n\001f\"\001T\032\006\n\001o\"\001T\032\007\n\002ci\"\001T\032\007\n\002co\"\001T\032\006\n\001h\"\001T\"\033\n\013forget_bias\022\005float\032\005%\000\000\200?\"\031\n\tcell_clip\022\005float\032\005%\000\000@@\"\030\n\014use_peephole\022\004bool\032\002(\000\"\020\n\001T\022\004type:\005\n\0032\001\001\n\246\002\n\021LSTMBlockCellGrad\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\022\006\n\001i\"\001T\022\007\n\002cs\"\001T\022\006\n\001f\"\001T\022\006\n\001o\"\001T\022\007\n\002ci\"\001T\022\007\n\002co\"\001T\022\014\n\007cs_grad\"\001T\022\013\n\006h_grad\"\001T\032\021\n\014cs_prev_grad\"\001T\032\n\n\005dicfo\"\001T\032\r\n\010wci_grad\"\001T\032\r\n\010wcf_grad\"\001T\032\r\n\010wco_grad\"\001T\"\024\n\014use_peephole\022\004bool\"\020\n\001T\022\004type:\005\n\0032\001\001")
  