class_docstr: A helper object to build a device infeed queue.
  
  The InfeedQueue builds the host-side and device-side Ops to enqueue and
  dequeue elements, respectively, and ensures that their types and
  shapes match.
class_docstr_tok: ["A","helper","object","to","build","a","device","infeed","queue",".","The","InfeedQueue","builds","the","host-side","and","device-side","Ops","to","enqueue","and","dequeue","elements",",","respectively",",","and","ensures","that","their","types","and","shapes","match","."]
docstr: Freezes the InfeedQueue so it can no longer be modified.
  
  The configuration is implicitly frozen before any host-side or
  device-side Ops are generated. The configuration cannot be frozen
  until the types and shapes of the tuple elements have been set.
  
  Raises:
    ValueError: if the types or shapes of the tuple elements have not been
    set.
code: def freeze(self):
      """Freezes the InfeedQueue so it can no longer be modified.
  
      The configuration is implicitly frozen before any host-side or
      device-side Ops are generated. The configuration cannot be frozen
      until the types and shapes of the tuple elements have been set.
  
      Raises:
        ValueError: if the types or shapes of the tuple elements have not been
        set.
      """
      self._frozen = True
      if self._tuple_types is None:
          raise ValueError(
              "Can't freeze an InfeedQueue without setting all tuple types."
          )
      if self._tuple_shapes is None:
          raise ValueError(
              "Can't freeze an InfeedQueue without setting all tuple shapes."
          )
      for shape in self._tuple_shapes:
          if shape.dims is None:
              raise ValueError(
                  "Can't freeze an InfeedQueue without setting all tuple shapes."
              )
      for policy in self._sharding_policies:
          policy.freeze()
      self._validate()
  
imports: import itertools
  import numpy
  from tensorflow.python.compiler.xla.experimental import xla_sharding
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.tpu import tpu_name_util
  from tensorflow.python.tpu import tpu_sharding
  from tensorflow.python.tpu.ops import tpu_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 15 for calling the InfeedQueue obj's underlying member method code with example usage: obj.freeze() without return types