class_docstr: Test class for `BinaryRecallAtFixedPrecision` metric.
class_docstr_tok: ["Test","class","for","`","BinaryRecallAtFixedPrecision","`","metric","."]
docstr: Test the differentiability of the metric, according to its `is_differentiable` attribute.
code: def test_binary_recall_at_fixed_precision_differentiability(
      self, inputs
  ):
      """Test the differentiability of the metric, according to its `is_differentiable` attribute."""
      preds, target = inputs
      self.run_differentiability_test(
          preds=preds,
          target=target,
          metric_module=BinaryRecallAtFixedPrecision,
          metric_functional=binary_recall_at_fixed_precision,
          metric_args={"min_precision": 0.5, "thresholds": None},
      )
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from scipy.special import softmax
  from sklearn.metrics import precision_recall_curve
  from torchmetrics.classification.recall_fixed_precision import BinaryRecallAtFixedPrecision, MulticlassRecallAtFixedPrecision, MultilabelRecallAtFixedPrecision, RecallAtFixedPrecision
  from torchmetrics.functional.classification.recall_fixed_precision import binary_recall_at_fixed_precision, multiclass_recall_at_fixed_precision, multilabel_recall_at_fixed_precision
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES
  from unittests.classification.inputs import _binary_cases, _multiclass_cases, _multilabel_cases
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester, inject_ignore_index, remove_ignore_index
label_desc: Use image node_id 3 for calling the TestBinaryRecallAtFixedPrecision obj's underlying member method code with example usage: obj.test_binary_recall_at_fixed_precision_differentiability(inputs) without return types