def pad(x, axis, front=False, back=False, value=<NUM_LIT:0>, count=<NUM_LIT:1>, name=None):
with tf.name_scope(name or "<STR_LIT>"):<EOL><INDENT>x = tf.convert_to_tensor(value=x, name="<STR_LIT:x>")<EOL>value = tf.convert_to_tensor(value=value, dtype=x.dtype, name="<STR_LIT:value>")<EOL>count = tf.convert_to_tensor(value=count, name="<STR_LIT:count>")<EOL>if not dtype_util.is_integer(count.dtype):<EOL><INDENT>raise TypeError("<STR_LIT>".format(<EOL>dtype_util.name(count.dtype)))<EOL><DEDENT>if not front and not back:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>ndims = (<EOL>tensorshape_util.rank(x.shape)<EOL>if tensorshape_util.rank(x.shape) is not None else tf.rank(<EOL>x, name="<STR_LIT>"))<EOL>axis = tf.convert_to_tensor(value=axis, name="<STR_LIT>")<EOL>axis_ = tf.get_static_value(axis)<EOL>if axis_ is not None:<EOL><INDENT>axis = axis_<EOL>if axis < <NUM_LIT:0>:<EOL><INDENT>axis = ndims + axis<EOL><DEDENT>count_ = tf.get_static_value(count)<EOL>if axis_ >= <NUM_LIT:0> or tensorshape_util.rank(x.shape) is not None:<EOL><INDENT>head = x.shape[:axis]<EOL>mid_dim_value = tf.compat.dimension_value(x.shape[axis])<EOL>if count_ is None or mid_dim_value is None:<EOL><INDENT>middle = tf.TensorShape(None)<EOL><DEDENT>else:<EOL><INDENT>middle = tf.TensorShape(mid_dim_value + count_ * (front + back))<EOL><DEDENT>tail = x.shape[axis + <NUM_LIT:1>:]<EOL>final_shape = head.concatenate(middle.concatenate(tail))<EOL><DEDENT>else:<EOL><INDENT>final_shape = None<EOL><DEDENT><DEDENT>else:<EOL><INDENT>axis = tf.where(axis < <NUM_LIT:0>, ndims + axis, axis)<EOL>final_shape = None<EOL><DEDENT>x = tf.pad(<EOL>tensor=x,<EOL>paddings=tf.one_hot(<EOL>indices=tf.stack([axis if front else -<NUM_LIT:1>, axis if back else -<NUM_LIT:1>]),<EOL>depth=ndims,<EOL>axis=<NUM_LIT:0>,<EOL>on_value=count,<EOL>dtype=tf.int32),<EOL>constant_values=value)<EOL>if final_shape is not None:<EOL><INDENT>tensorshape_util.set_shape(x, final_shape)<EOL><DEDENT>return x<EOL><DEDENT>
Pads `value` to the front and/or back of a `Tensor` dim, `count` times.

    Args:
      x: `Tensor` input.
      axis: Scalar `int`-like `Tensor` representing the single dimension to pad.
        (Negative indexing is supported.)
      front: Python `bool`; if `True` the beginning of the `axis` dimension is
        padded with `value`, `count` times. If `False` no front padding is made.
      back: Python `bool`; if `True` the end of the `axis` dimension is padded
        with `value`, `count` times. If `False` no end padding is made.
      value: Scalar `int`-like `Tensor` representing the actual value added to the
        front and/or back of the `axis` dimension of `x`.
      count: Scalar `int`-like `Tensor` representing number of elements added to
        the front and/or back of the `axis` dimension of `x`. E.g., if `front =
        back = True` then `2 * count` elements are added.
      name: Python `str` name prefixed to Ops created by this function.

    Returns:
      pad: The padded version of input `x`.

    Raises:
      ValueError: if both `front` and `back` are `False`.
      TypeError: if `count` is not `int`-like.