class_docstr: Accumulator for TF metrics.
  
  Attributes:
    inputs: Accumulated batch of inputs. The inputs are stored in a
      multi-dimensional list. The first dimension is used to index the
      associated output (for single-output models this will only have one item).
      The second dimension is used to store the args used by the combiner. For
      example the args might be a tf.Example if feeding a model or they might be
      (y_true, y_pred, example_weight) for calling update_state directly.
      Batching is done on the last dimension.
    weights: Accumulated weights. The weights are stored in a multi-dimensional
      list where the first dimension is used to index the associated output (for
      single-output models this will only have one item). The second dimension
      is used to store the accumulated weights for each metric associated with
      the output dimension.
    size_estimator: Batch size estimator.
    desired_batch_size: Desired batch size.
class_docstr_tok: ["Accumulator","for","TF","metrics",".","Attributes",":","inputs",":","Accumulated","batch","of","inputs",".","The","inputs","are","stored","in","a","multi-dimensional","list",".","The","first","dimension","is","used","to","index","the","associated","output","(","for","single-output","models","this","will","only","have","one","item",")",".","The","second","dimension","is","used","to","store","the","args","used","by","the","combiner",".","For","example","the","args","might","be","a","tf.Example","if","feeding","a","model","or","they","might","be","(","y_true",",","y_pred",",","example_weight",")","for","calling","update_state","directly",".","Batching","is","done","on","the","last","dimension",".","weights",":","Accumulated","weights",".","The","weights","are","stored","in","a","multi-dimensional","list","where","the","first","dimension","is","used","to","index","the","associated","output","(","for","single-output","models","this","will","only","have","one","item",")",".","The","second","dimension","is","used","to","store","the","accumulated","weights","for","each","metric","associated","with","the","output","dimension",".","size_estimator",":","Batch","size","estimator",".","desired_batch_size",":","Desired","batch","size","."]
docstr: Clears currently stored inputs.
docstr_tok: ["Clears","currently","stored","inputs","."]
code: def clear_inputs(self):
      """Clears currently stored inputs."""
      for output_index in range(len(self._inputs)):
          for i in range(len(self._inputs[output_index])):
              del self._inputs[output_index][i][:]
      self._size_estimator.clear()
  
imports: from typing import Any, Callable, List, Optional, Tuple, Union
  import numpy
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.utils import size_estimator
label_desc: Use image node_id 5 for calling the TFMetricsAccumulator obj's underlying member method code with example usage: obj.clear_inputs() without return types