code: def _right_pad_or_crop(tensor, shape):
      input_shape = tf.shape(tensor)
      shape = tf.convert_to_tensor(shape, dtype=tf.dtypes.int32)
      with tf.control_dependencies(
          [
              tf.debugging.assert_less_equal(
                  tf.size(shape), tf.size(input_shape)
              )
          ]
      ):
          shape = tf.identity(shape)
      shape = tf.concat(
          [input_shape[: tf.size(input_shape) - tf.size(shape)], shape],
          0,
      )
  
      pad_sizes = tf.math.maximum(shape - input_shape, 0)
      pad_sizes = tf.expand_dims(pad_sizes, -1)
      pad_sizes = tf.concat(
          [tf.zeros(pad_sizes.shape, dtype=tf.dtypes.int32), pad_sizes],
          -1,
      )
      tensor = tf.pad(tensor, pad_sizes, constant_values=0)
  
      crop_tensor = tf.zeros(shape.shape, dtype=tf.dtypes.int32)
      tensor = tf.slice(tensor, crop_tensor, shape)
      return tensor
  
imports: import math
  from typing import Union, Optional, Tuple, List, Literal, Sequence, Callable
  import tensorflow
  from ivy.func_wrapper import inputs_to_ivy_arrays, output_to_native_arrays, with_unsupported_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _get_size
label_desc: Use image node_id 39 for calling a global function with example usage: _right_pad_or_crop(tensor, shape) and returns: tensor