class_docstr: Focal Loss for inbalanced classification.
  Focal loss was described in https://arxiv.org/abs/1708.02002
  
  Parameters
  ----------
  axis : int, default -1
      The axis to sum over when computing softmax and entropy.
  alpha : float, default 0.25
      The alpha which controls loss curve.
  gamma : float, default 2
      The gamma which controls loss curve.
  sparse_label : bool, default True
      Whether label is an integer array instead of probability distribution.
  from_logits : bool, default False
      Whether input is a log probability (usually from log_softmax) instead.
  batch_axis : int, default 0
      The axis that represents mini-batch.
  weight : float or None
      Global scalar weight for loss.
  num_class : int
      Number of classification categories. It is required is `sparse_label` is `True`.
  eps : float
      Eps to avoid numerical issue.
  size_average : bool, default True
      If `True`, will take mean of the output loss on every axis except `batch_axis`.
  
  Inputs:
      - **pred**: the prediction tensor, where the `batch_axis` dimension
        ranges over batch size and `axis` dimension ranges over the number
        of classes.
      - **label**: the truth tensor. When `sparse_label` is True, `label`'s
        shape should be `pred`'s shape with the `axis` dimension removed.
        i.e. for `pred` with shape (1,2,3,4) and `axis = 2`, `label`'s shape
        should be (1,2,4) and values should be integers between 0 and 2. If
        `sparse_label` is False, `label`'s shape must be the same as `pred`
        and values should be floats in the range `[0, 1]`.
      - **sample_weight**: element-wise weighting tensor. Must be broadcastable
        to the same shape as label. For example, if label has shape (64, 10)
        and you want to weigh each sample in the batch separately,
        sample_weight should have shape (64, 1).
  Outputs:
      - **loss**: loss tensor with shape (batch_size,). Dimensions other than
        batch_axis are averaged out.
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docstr: Loss forward
docstr_tok: ["Loss","forward"]
code: def hybrid_forward(self, F, pred, label, sample_weight=None):
      """Loss forward"""
      if not self._from_logits:
          pred = F.sigmoid(pred)
      if self._sparse_label:
          one_hot = F.one_hot(label, self._num_class)
      else:
          one_hot = label > 0
      pt = F.where(one_hot, pred, 1 - pred)
      t = F.ones_like(one_hot)
      alpha = F.where(one_hot, self._alpha * t, (1 - self._alpha) * t)
      loss = (
          -alpha
          * ((1 - pt) ** self._gamma)
          * F.log(F.minimum(pt + self._eps, 1))
      )
      loss = _apply_weighting(F, loss, self._weight, sample_weight)
      if self._size_average:
          return F.mean(loss, axis=self._batch_axis, exclude=True)
      else:
          return F.sum(loss, axis=self._batch_axis, exclude=True)
  
imports: from __future__ import absolute_import
  from mxnet import gluon
  from mxnet import nd
  from mxnet.gluon.loss import Loss, _apply_weighting, _reshape_like
label_desc: Use image node_id 2 for calling the FocalLoss obj's underlying member method code with example usage: obj.hybrid_forward(F, pred, label, sample_weight) and returns: F, F