torch.distributions.binomial 的源代码

```html

import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import (
    broadcast_all,
    lazy_property,
    logits_to_probs,
    probs_to_logits,
)

__all__ = ["Binomial"]


def _clamp_by_zero(x):
    # 类似于 clamp(x, min=0) 但 grad 在 0 处为 0.5
    return (x.clamp(min=0) + x - x.clamp(max=0)) / 2


class Binomial(Distribution):
    r"""
    创建一个由 :attr:`total_count` 和 :attr:`probs` 或 :attr:`logits`（但不能同时使用两者）参数化的二项分布。:attr:`total_count` 必须与 :attr:`probs`/:attr:`logits` 广播兼容。

    示例::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Binomial(100, torch.tensor([0 , .2, .8, 1]))
        >>> x = m.sample()
        tensor([   0.,   22.,   71.,  100.])

        >>> m = Binomial(torch.tensor([[5.], [10.]]), torch.tensor([0.5, 0.8]))
        >>> x = m.sample()
        tensor([[ 4.,  5.],
                [ 7.,  6.]])

    参数:
        total_count (int 或 Tensor): 伯努利试验次数
        probs (Tensor): 事件概率
        logits (Tensor): 事件对数几率
    """
    arg_constraints = {
        "total_count": constraints.nonnegative_integer,
        "probs": constraints.unit_interval,
        "logits": constraints.real,
    }
    has_enumerate_support = True

    def __init__(self, total_count=1, probs=None, logits=None, validate_args=None):
        if (probs is None) == (logits is None):
            raise ValueError(
                "Either `probs` or `logits` must be specified, but not both."
            )
        if probs is not None:
            (
                self.total_count,
                self.probs,
            ) = broadcast_all(total_count, probs)
            self.total_count = self.total_count.type_as(self.probs)
        else:
            (
                self.total_count,
                self.logits,
            ) = broadcast_all(total_count, logits)
            self.total_count = self.total_count.type_as(self.logits)

        self._param = self.probs if probs is not None else self.logits
        batch_shape = self._param.size()
        super().__init__(batch_shape, validate_args=validate_args)

    def expand(self, batch_shape, _instance=None):
        new = self._get_checked_instance(Binomial, _instance)
        batch_shape = torch.Size(batch_shape)
        new.total_count = self.total_count.expand(batch_shape)
        if "probs" in self.__dict__:
            new.probs = self.probs.expand(batch_shape)
            new._param = new.probs
        if "logits" in self.__dict__:
            new.logits = self.logits.expand(batch_shape)
            new._param = new.logits
        super(Binomial, new).__init__(batch_shape, validate_args=False)
        new._validate_args = self._validate_args
        return new

    def _new(self, *args, **kwargs):
        return self._param.new(*args, **kwargs)

    @constraints.dependent_property(is