docstr: ivy.Container instance method variant of ivy.conv3d. This method
  simply wraps the function, and so the docstring for ivy.conv3d also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      Input volume *[batch_size,d,h,w,d_in]*.
  filters
      Convolution filters *[fdfh,fw,d_in,d_out]*.
  strides
      The stride of the sliding window for each dimension of input.
  padding
      "SAME" or "VALID" indicating the algorithm, or list indicating
      the per-dimension paddings.
  data_format
      "NDHWC" or "NCDHW". Defaults to "NDHWC".
  filter_format
      Either "channel_first" or "channel_last". Defaults to "channel_last".
  x_dilations
      The dilation factor for each dimension of input. (Default value = 1)
  dilations
      The dilation factor for each dimension of input. (Default value = 1)
  bias
      Bias array of shape *[d_out]*.
  out
      optional output array, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      The result of the convolution operation.
  
  Examples
  --------
  >>> x = ivy.Container(a = ivy.full((1, 2, 3, 3, 1),0.5),                              b = ivy.full((1, 2, 5, 5, 1),1.))
  
  >>> filters = ivy.ones((3, 3, 3, 1, 1))
  
  >>> result = x.conv3d(filters, 2, 'SAME')
  >>> print(result)
  {
      a: ivy.array([[[[[4.],[4.]],[[4.],[4.]]]]]),
      b: ivy.array([[[[[8.],[12.],[8.]],[[12.],[18.],[12.]],[[8.],[12.],[8.]]]]])
  }
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code: def conv3d(
      self: ivy.Container,
      filters: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      strides: Union[int, Tuple[int, int, int], ivy.Container],
      padding: Union[str, ivy.Container],
      /,
      *,
      data_format: str = "NDHWC",
      filter_format: str = "channel_last",
      x_dilations: Union[int, Tuple[int, int, int]] = 1,
      dilations: Union[int, Tuple[int, int, int]] = 1,
      key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
      to_apply: bool = True,
      prune_unapplied: bool = False,
      map_sequences: bool = False,
      bias: Optional[ivy.Container] = None,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.conv3d. This method
          simply wraps the function, and so the docstring for ivy.conv3d also
          applies to this method with minimal changes.
  
          Parameters
          ----------
          x
              Input volume *[batch_size,d,h,w,d_in]*.
          filters
              Convolution filters *[fdfh,fw,d_in,d_out]*.
          strides
              The stride of the sliding window for each dimension of input.
          padding
              "SAME" or "VALID" indicating the algorithm, or list indicating
              the per-dimension paddings.
          data_format
              "NDHWC" or "NCDHW". Defaults to "NDHWC".
          filter_format
              Either "channel_first" or "channel_last". Defaults to "channel_last".
          x_dilations
              The dilation factor for each dimension of input. (Default value = 1)
          dilations
              The dilation factor for each dimension of input. (Default value = 1)
          bias
              Bias array of shape *[d_out]*.
          out
              optional output array, for writing the result to. It must have a shape
              that the inputs broadcast to.
  
          Returns
          -------
          ret
              The result of the convolution operation.
  
          Examples
          --------
          >>> x = ivy.Container(a = ivy.full((1, 2, 3, 3, 1),0.5),\
                                b = ivy.full((1, 2, 5, 5, 1),1.))
  
          >>> filters = ivy.ones((3, 3, 3, 1, 1))
  
          >>> result = x.conv3d(filters, 2, 'SAME')
          >>> print(result)
          {
              a: ivy.array([[[[[4.],[4.]],[[4.],[4.]]]]]),
              b: ivy.array([[[[[8.],[12.],[8.]],[[12.],[18.],[12.]],[[8.],[12.],[8.]]]]])
          }
          """
      return self._static_conv3d(
          self,
          filters,
          strides,
          padding,
          data_format=data_format,
          filter_format=filter_format,
          x_dilations=x_dilations,
          dilations=dilations,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          bias=bias,
          out=out,
      )
  
imports: from typing import Optional, Tuple, Union, List, Callable, Dict, Sequence
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 26 for calling the _ContainerWithLayers obj's underlying member method code with example usage: obj.conv3d() and returns: self