class_docstr: Configuration for parsing a variable-length input feature into a `Tensor`.
  
  The resulting `Tensor` of parsing a single `SequenceExample` or `Example` has
  a static `shape` of `[None] + shape` and the specified `dtype`.
  The resulting `Tensor` of parsing a `batch_size` many `Example`s has
  a static `shape` of `[batch_size, None] + shape` and the specified `dtype`.
  The entries in the `batch` from different `Examples` will be padded with
  `default_value` to the maximum length present in the `batch`.
  
  To treat a sparse input as dense, provide `allow_missing=True`; otherwise,
  the parse functions will fail on any examples missing this feature.
  
  Fields:
    shape: Shape of input data for dimension 2 and higher. First dimension is
      of variable length `None`.
    dtype: Data type of input.
    allow_missing: Whether to allow this feature to be missing from a feature
      list item. Is available only for parsing `SequenceExample` not for
      parsing `Examples`.
    default_value: Scalar value to be used to pad multiple `Example`s to their
      maximum length. Irrelevant for parsing a single `Example` or
      `SequenceExample`. Defaults to "" for dtype string and 0 otherwise
      (optional).
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code: def __new__(
      cls, shape, dtype, allow_missing=False, default_value=None
  ):
      return super(FixedLenSequenceFeature, cls).__new__(
          cls, shape, dtype, allow_missing, default_value
      )
  
imports: import collections
  import re
  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
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops.ragged import ragged_math_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling the FixedLenSequenceFeature obj's underlying member method code with example usage: obj.__new__(cls, shape, dtype, allow_missing, default_value) and returns: super