code: def pad(
      x, axis, front=False, back=False, value=0, count=1, name=None
  ):
      """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.
      """
      with tf.name_scope(name or "pad"):
          x = tf.convert_to_tensor(x, name="x")
          value = tf.convert_to_tensor(
              value, dtype=x.dtype, name="value"
          )
          count = ps.convert_to_shape_tensor(count, name="count")
          if not dtype_util.is_integer(count.dtype):
              raise TypeError(
                  "`count.dtype` (`{}`) must be `int`-like.".format(
                      dtype_util.name(count.dtype)
                  )
              )
          if not front and not back:
              raise ValueError(
                  "At least one of `front`, `back` must be `True`."
              )
          ndims = (
              tensorshape_util.rank(x.shape)
              if tensorshape_util.rank(x.shape) is not None
              else tf.rank(x, name="ndims")
          )
          axis = ps.convert_to_shape_tensor(axis, name="axis")
          axis_ = tf.get_static_value(axis)
          if axis_ is not None:
              axis = axis_
              if axis < 0:
                  axis = ndims + axis
              count_ = tf.get_static_value(count)
              if (
                  axis_ >= 0
                  or tensorshape_util.rank(x.shape) is not None
              ):
                  head = x.shape[:axis]
                  mid_dim_value = tf.compat.dimension_value(
                      x.shape[axis]
                  )
                  if count_ is None or mid_dim_value is None:
                      middle = tf.TensorShape(None)
                  else:
                      middle = tf.TensorShape(
                          mid_dim_value + count_ * (front + back)
                      )
                  tail = x.shape[axis + 1 :]
                  final_shape = tensorshape_util.concatenate(
                      head, tensorshape_util.concatenate(middle, tail)
                  )
              else:
                  final_shape = None
          else:
              axis = tf.where(axis < 0, ndims + axis, axis)
              final_shape = None
          x = tf.pad(
              x,
              paddings=ps.one_hot(
                  indices=ps.stack(
                      [axis if front else -1, axis if back else -1]
                  ),
                  depth=ndims,
                  axis=0,
                  on_value=count,
                  dtype=tf.int32,
              ),
              constant_values=value,
          )
          if final_shape is not None:
              tensorshape_util.set_shape(x, final_shape)
          return x
  
imports: import functools
  import hashlib
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 38 for calling a global function with example usage: pad(x, axis, front, back, value, count, name) and returns: x