class_docstr: Handles input pipeline for SimdMeshImpl.
class_docstr_tok: ["Handles","input","pipeline","for","SimdMeshImpl","."]
docstr: Generate enqueue ops to enqueue all_laidout_tensors.
docstr_tok: ["Generate","enqueue","ops","to","enqueue","all_laidout_tensors","."]
code: def _enqueue_laidout_tensors(self, all_laidout_tensors):
      """Generate enqueue ops to enqueue all_laidout_tensors."""
  
      def _tpu_ordinal_function_impl(pnum):
          return self._p_dev.ordered_ordinals[pnum]
  
      def _placement_function_impl(pnum):
          return self._p_dev.ordered_hosts[pnum]
  
      laidout_tensors0 = all_laidout_tensors[0]
      infeed_queue = tpu_feed.InfeedQueue(
          number_of_tuple_elements=len(laidout_tensors0),
          tuple_types=[x.dtype for x in laidout_tensors0],
          tuple_shapes=[x.shape for x in laidout_tensors0],
      )
      enqueue_ops = infeed_queue.generate_enqueue_ops(
          all_laidout_tensors,
          tpu_ordinal_function=_tpu_ordinal_function_impl,
          placement_function=_placement_function_impl,
      )
  
      return infeed_queue, enqueue_ops
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import threading
  import time
  import numpy
  from six.moves import range
  from six.moves import zip
  import tensorflow.compat.v1
  from tensorflow.python.framework import ops
  from tensorflow.python.tpu import tpu_feed
label_desc: Use image node_id 7 for calling the SimdMeshImplInputReader obj's underlying member method code with example usage: obj._enqueue_laidout_tensors(all_laidout_tensors) and returns: infeed_queue,  enqueue_ops, self, self