(torch.Tensor method) ](tensors.html#torch.Tensor.mean)

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  * [ median() (in module torch) ](generated/torch.median.html#torch.median) , [ [1] ](generated/torch.median.html#torch.median)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.median)
  * [ memory_allocated() (in module torch.cuda) ](cuda.html#torch.cuda.memory_allocated)
  * [ memory_cached() (in module torch.cuda) ](cuda.html#torch.cuda.memory_cached)
  * [ memory_reserved() (in module torch.cuda) ](cuda.html#torch.cuda.memory_reserved)
  * [ memory_snapshot() (in module torch.cuda) ](cuda.html#torch.cuda.memory_snapshot)
  * [ memory_stats() (in module torch.cuda) ](cuda.html#torch.cuda.memory_stats)
  * [ memory_summary() (in module torch.cuda) ](cuda.html#torch.cuda.memory_summary)
  * [ meshgrid() (in module torch) ](generated/torch.meshgrid.html#torch.meshgrid)
  * [ min() (in module torch) ](generated/torch.min.html#torch.min) , [ [1] ](generated/torch.min.html#torch.min) , [ [2] ](generated/torch.min.html#torch.min)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.min)
  * [ MinMaxObserver (class in torch.quantization) ](quantization.html#torch.quantization.MinMaxObserver)
  * [ mixture_distribution() (torch.distributions.mixture_same_family.MixtureSameFamily property) ](distributions.html#torch.distributions.mixture_same_family.MixtureSameFamily.mixture_distribution)
  * [ MixtureSameFamily (class in torch.distributions.mixture_same_family) ](distributions.html#torch.distributions.mixture_same_family.MixtureSameFamily)
  * [ mm() (in module torch) ](generated/torch.mm.html#torch.mm)
    * [ (in module torch.sparse) ](sparse.html#torch.sparse.mm)
    * [ (torch.sparse.FloatTensor method) ](sparse.html#torch.sparse.FloatTensor.mm)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.mm)
  * [ mnasnet0_5() (in module torchvision.models) ](torchvision/models.html#torchvision.models.mnasnet0_5)
  * [ mnasnet0_75() (in module torchvision.models) ](torchvision/models.html#torchvision.models.mnasnet0_75)
  * [ mnasnet1_0() (in module torchvision.models) ](torchvision/models.html#torchvision.models.mnasnet1_0)
  * [ mnasnet1_3() (in module torchvision.models) ](torchvision/models.html#torchvision.models.mnasnet1_3)
  * [ MNIST (class in torchvision.datasets) ](torchvision/datasets.html#torchvision.datasets.MNIST)
  * [ mobilenet_v2() (in module torchvision.models) ](torchvision/models.html#torchvision.models.mobilenet_v2)
  * [ mode() (in module torch) ](generated/torch.mode.html#torch.mode)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.mode)
  * [ Module (class in torch.nn) ](generated/torch.nn.Module.html#torch.nn.Module)
  * [ Module.to() (in module torch.nn) ](generated/torch.nn.Module.html#torch.nn.Module.to) , [ [1] ](generated/torch.nn.Module.html#torch.nn.Module.to) , [ [2] ](generated/torch.nn.Module.html#torch.nn.Module.to) , [ [3] ](generated/torch.nn.Module.html#torch.nn.Module.to)
  * [ ModuleDict (class in torch.nn) ](generated/torch.nn.ModuleDict.html#torch.nn.ModuleDict)
  * [ ModuleList (class in torch.nn) ](generated/torch.nn.ModuleList.html#torch.nn.ModuleList)
  * [ modules() (torch.jit.ScriptModule method) ](generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule.modules)
    * [ (torch.nn.Flatten method) ](generated/torch.nn.Flatten.html#torch.nn.Flatten.modules)
    * [ (torch.nn.Module method) ](generated/torch.nn.Module.html#torch.nn.Module.modules)
  * [ MovingAverageMinMaxObserver (class in torch.quantization) ](quantization.html#torch.quantization.MovingAverageMinMaxObserver)
  * [ MovingAveragePerChannelMinMaxObserver (class in torch.quantization) ](quantization.html#torch.quantization.MovingAveragePerChannelMinMaxObserver)
  * [ mse_loss() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.mse_loss)
  * [ MSELoss (class in torch.nn) ](generated/torch.nn.MSELoss.html#torch.nn.MSELoss)
  * [ mul() (in module torch) ](generated/torch.mul.html#torch.mul) , [ [1] ](generated/torc