code: def _lstm_block_cell(
      x,
      cs_prev,
      h_prev,
      w,
      b,
      wci=None,
      wcf=None,
      wco=None,
      forget_bias=None,
      cell_clip=None,
      use_peephole=None,
      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, ci, f, 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.
        b: A `Tensor`. Must have the same type as `x`. The bias vector.
        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.
        forget_bias: An optional `float`. Defaults to `1`. The forget gate bias.
        cell_clip: An optional `float`. Defaults to `-1` (no clipping).
          Value to clip the 'cs' value to. Disable by setting to negative value.
        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.
  
      Raises:
        ValueError: If cell_size is None.
      """
      if wci is None:
          cell_size = int(cs_prev.shape.with_rank(2)[1])
          if cell_size is None:
              raise ValueError(
                  "cell_size from `cs_prev` should not be None."
              )
          wci = tf.constant(0, dtype=tf.float32, shape=[cell_size])
          wcf = wci
          wco = wci
  
      return tf.raw_ops.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 if cell_clip is not None else -1,
          use_peephole=use_peephole,
          name=name,
      )
  
imports: import tensorflow.compat.v1
  import tf_slim
  from tensorflow.python.ops import gen_nn_ops
label_desc: Use image node_id 5 for calling a global function with example usage: _lstm_block_cell(x, cs_prev, h_prev, w, b, wci, wcf, wco, forget_bias, cell_clip, use_peephole, name) and returns: tf