class_docstr: A dynamically quantized conv module with floating point tensors as inputs and outputs.
  
  For details on input arguments, parameters, and implementation see
  :class:`~torch.nn.Conv3d` and :class:`~torch.ao.nn.quantized.dynamic.Conv3d` and
  
  Attributes:
      weight (Tensor):     packed tensor derived from the learnable weight
                           parameter.
      scale (Tensor):      scalar for the output scale
      zero_point (Tensor): scalar for the output zero point
  
  See :class:`~torch.nn.Conv3d` for other attributes.
  
  Examples::
  
      >>> # xdoctest: +SKIP
      >>> # With square kernels and equal stride
      >>> m = nn.quantized.dynamic.Conv3d(16, 33, 3, stride=2)
      >>> # non-square kernels and unequal stride and with padding
      >>> m = nn.quantized.dynamic.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
      >>> # non-square kernels and unequal stride and with padding and dilation
      >>> m = nn.quantized.dynamic.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
      >>> input = torch.randn(20, 16, 56, 56, 56)
      >>> output = m(input)
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code: def _get_name(self):
      return "DynamicQuantizedConv3d"
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch._ops import ops
  from torch.nn.common_types import _size_1_t
  from torch.nn.modules.utils import _single, _pair, _triple
  from torch.ao.nn.quantized.modules.conv import _reverse_repeat_padding
  import torch.ao.nn.quantized
  import warnings
label_desc: Use image node_id 2 for calling the Conv3d obj's underlying member method code with example usage: obj._get_name() and returns: str