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 ops.name_scope(name, "pad", [x, value, count]):
          x = ops.convert_to_tensor(x, name="x")
          value = ops.convert_to_tensor(
              value, dtype=x.dtype, name="value"
          )
          count = ops.convert_to_tensor(count, name="count")
          if not count.dtype.is_integer:
              raise TypeError(
                  "`count.dtype` (`{}`) must be `int`-like.".format(
                      count.dtype.name
                  )
              )
          if not front and not back:
              raise ValueError(
                  "At least one of `front`, `back` must be `True`."
              )
          ndims = (
              x.shape.ndims
              if x.shape.ndims is not None
              else array_ops.rank(x, name="ndims")
          )
          axis = ops.convert_to_tensor(axis, name="axis")
          axis_ = tensor_util.constant_value(axis)
          if axis_ is not None:
              axis = axis_
              if axis < 0:
                  axis = ndims + axis
              count_ = tensor_util.constant_value(count)
              if axis_ >= 0 or x.shape.ndims is not None:
                  head = x.shape[:axis]
                  middle = tensor_shape.TensorShape(
                      None
                      if count_ is None
                      else (
                          tensor_shape.dimension_at_index(x.shape, axis)
                          + count_ * (front + back)
                      )
                  )
                  tail = x.shape[axis + 1 :]
                  final_shape = head.concatenate(
                      middle.concatenate(tail)
                  )
              else:
                  final_shape = None
          else:
              axis = array_ops.where_v2(axis < 0, ndims + axis, axis)
              final_shape = None
          x = array_ops.pad(
              x,
              paddings=array_ops.one_hot(
                  indices=array_ops_stack.stack(
                      [axis if front else -1, axis if back else -1]
                  ),
                  depth=ndims,
                  axis=0,
                  on_value=count,
                  dtype=dtypes.int32,
              ),
              constant_values=value,
          )
          if final_shape is not None:
              x.set_shape(final_shape)
          return x
  
imports: import functools
  import hashlib
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 31 for calling a global function with example usage: pad(x, axis, front, back, value, count, name) and returns: x