class_docstr: A metric which returns the output of the given function on each batch.
  
  Args:
      name (str): The name of the metric.
      metric_function (func): A metric function('y_pred', 'y_true') to wrap.
class_docstr_tok: ["A","metric","which","returns","the","output","of","the","given","function","on","each","batch",".","Args",":","name","(","str",")",":","The","name","of","the","metric",".","metric_function","(","func",")",":","A","metric","function","(","'y_pred","'",",","'y_true","'",")","to","wrap","."]
code: def __init__(self, name, metric_function):
      super(BatchLambda, self).__init__(name)
      self._metric_function = metric_function
  
imports: import torchbearer
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
  import torch
label_desc: Use image node_id 1 to create a new BatchLambda object from inherited base classes: Metric with example: obj = BatchLambda(name, metric_function)