in torch.nn.quantized) ](quantization.html#torch.nn.quantized.BatchNorm2d)
  * [ BatchNorm3d (class in torch.nn) ](generated/torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d)
    * [ (class in torch.nn.quantized) ](quantization.html#torch.nn.quantized.BatchNorm3d)
  * [ BatchSampler (class in torch.utils.data) ](data.html#torch.utils.data.BatchSampler)
  * [ BCELoss (class in torch.nn) ](generated/torch.nn.BCELoss.html#torch.nn.BCELoss)
  * [ BCEWithLogitsLoss (class in torch.nn) ](generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss)
  * [ Bernoulli (class in torch.distributions.bernoulli) ](distributions.html#torch.distributions.bernoulli.Bernoulli)
  * [ bernoulli() (in module torch) ](generated/torch.bernoulli.html#torch.bernoulli)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bernoulli)
  * [ bernoulli_() (torch.Tensor method) ](tensors.html#torch.Tensor.bernoulli_)
  * [ Beta (class in torch.distributions.beta) ](distributions.html#torch.distributions.beta.Beta)
  * [ bfloat16() (torch.FloatStorage method) ](storage.html#torch.FloatStorage.bfloat16)
    * [ (torch.jit.ScriptModule method) ](generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule.bfloat16)
    * [ (torch.nn.Flatten method) ](generated/torch.nn.Flatten.html#torch.nn.Flatten.bfloat16)
    * [ (torch.nn.Module method) ](generated/torch.nn.Module.html#torch.nn.Module.bfloat16)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bfloat16)
  * [ Bilinear (class in torch.nn) ](generated/torch.nn.Bilinear.html#torch.nn.Bilinear)
  * [ bilinear() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.bilinear)
  * [ binary_cross_entropy() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.binary_cross_entropy)

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  * [ binary_cross_entropy_with_logits() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.binary_cross_entropy_with_logits)
  * [ bincount() (in module torch) ](generated/torch.bincount.html#torch.bincount)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bincount)
  * [ Binomial (class in torch.distributions.binomial) ](distributions.html#torch.distributions.binomial.Binomial)
  * [ bitwise_and() (in module torch) ](generated/torch.bitwise_and.html#torch.bitwise_and)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bitwise_and)
  * [ bitwise_and_() (torch.Tensor method) ](tensors.html#torch.Tensor.bitwise_and_)
  * [ bitwise_not() (in module torch) ](generated/torch.bitwise_not.html#torch.bitwise_not)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bitwise_not)
  * [ bitwise_not_() (torch.Tensor method) ](tensors.html#torch.Tensor.bitwise_not_)
  * [ bitwise_or() (in module torch) ](generated/torch.bitwise_or.html#torch.bitwise_or)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bitwise_or)
  * [ bitwise_or_() (torch.Tensor method) ](tensors.html#torch.Tensor.bitwise_or_)
  * [ bitwise_xor() (in module torch) ](generated/torch.bitwise_xor.html#torch.bitwise_xor)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bitwise_xor)
  * [ bitwise_xor_() (torch.Tensor method) ](tensors.html#torch.Tensor.bitwise_xor_)
  * [ blackman_window() (in module torch) ](generated/torch.blackman_window.html#torch.blackman_window)
  * [ block_diag() (in module torch) ](generated/torch.block_diag.html#torch.block_diag)
  * [ bmm() (in module torch) ](generated/torch.bmm.html#torch.bmm)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bmm)
  * [ bool() (torch.FloatStorage method) ](storage.html#torch.FloatStorage.bool)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.bool)
  * [ BoolTensor (class in torch) ](tensors.html#torch.BoolTensor)
  * [ BoolTensor.all() (in module torch) ](tensors.html#torch.BoolTensor.all) , [ [1] ](tensors.html#torch.BoolTensor.all)
  * [ BoolTensor.any() (in module torch) ](tensors.html#torch.BoolTensor.any) , [ [1] ](tensors.html#torch.BoolTensor.any)
  * [ broadcast() (in module torch.