docstr: update the histogram using the current batch
docstr_tok: ["update","the","histogram","using","the","current","batch"]
code: def _compute_histogram(self, x, momentum):
      """
      update the histogram using the current batch
      """
      num_bins = self.histogram.size(0)
      x_detached = x.detach()
      self.bin_width = (
          self._max_val - self._min_val
      ) / num_bins  # Adjusted formula
      lo = torch.floor(
          (x_detached - self._min_val) / self.bin_width
      ).long()  # Add cast to avoid errors
      hi = (lo + 1).clamp(min=0, max=num_bins - 1)
      hist = x.new_zeros(num_bins)
      alpha = (
          1.0
          - (x_detached - self._min_val - lo.float() * self.bin_width)
          / self.bin_width
      ).to(
          dtype=hist.dtype
      )  # Added cast to avoid errors
      hist.index_add_(0, lo, alpha)
      hist.index_add_(0, hi, 1.0 - alpha)
      hist = hist / (hist.sum() + 1e-6)
      self.histogram = c_f.to_device(
          self.histogram, tensor=hist, dtype=hist.dtype
      )  # Line added to avoid errors
      self.histogram = (
          1.0 - momentum
      ) * self.histogram + momentum * hist
  
imports: import torch
  import torch.nn
  from pytorch_metric_learning.utils import common_functions
  from pytorch_metric_learning.utils import loss_and_miner_utils
  import unittest
  from pytorch_metric_learning.losses import DynamicSoftMarginLoss
  from ..None import TEST_DEVICE, TEST_DTYPES
  from ..zzz_testing_utils.testing_utils import angle_to_coord
label_desc: Use image node_id 4 for calling the OriginalImplementationDynamicSoftMarginLoss obj's underlying member method code with example usage: obj._compute_histogram(x, momentum) without return types