class_docstr: Materializes reports.
  
  Specifically it materializes a subnetwork's :class:`adanet.subnetwork.Report`
  instances into :class:`adanet.subnetwork.MaterializedReport` instances.
  
  Requires an input function `input_fn` that returns a tuple of:
  
  * features: Dictionary of string feature name to `Tensor`.
  * labels: `Tensor` of labels.
  
  Args:
    input_fn: The input function.
    steps: Number of steps for which to materialize the ensembles. If an
      `OutOfRangeError` occurs, materialization stops. If set to None, will
      iterate the dataset until all inputs are exhausted.
  
  Returns:
    A `ReportMaterializer` instance.
code: def __init__(self, input_fn, steps=None):
      self._input_fn = input_fn
      self._steps = steps
      super(ReportMaterializer, self).__init__()
  
file_path: adanet/adanet/core/report_materializer.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from absl import logging
  from adanet import subnetwork
  from adanet import tf_compat
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new ReportMaterializer object from inherited base classes: object with example: obj = ReportMaterializer(input_fn, steps)