class_docstr: Example count.
  
  Note that although the example_count is independent of the model, this metric
  will be associated with a model for consistency with other metrics.
class_docstr_tok: ["Example","count",".","Note","that","although","the","example_count","is","independent","of","the","model",",","this","metric","will","be","associated","with","a","model","for","consistency","with","other","metrics","."]
docstr: Initializes example count.
  
  Args:
    name: Metric name.
docstr_tok: ["Initializes","example","count",".","Args",":","name",":","Metric","name","."]
code: def __init__(self, name: str = EXAMPLE_COUNT_NAME):
      """Initializes example count.
  
      Args:
        name: Metric name.
      """
  
      super().__init__(example_count, name=name)
  
file_path: model-analysis/tensorflow_model_analysis/metrics/example_count.py
imports: from typing import Optional, Dict, Iterable, List
  import apache_beam
  import numpy
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.utils import util
label_desc: Use image node_id 1 to create a new ExampleCount object from inherited base classes: metric_types with example: obj = ExampleCount(name)