docstr: ivy.Container static method variant of ivy.conv3d_transpose. This
  method simply wraps the function, and so the docstring for
  ivy.conv3d_transpose also applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      Input container with leaves of volume *[batch_size,d,h,w,d_in]*
      or *[batch_size,d_in,d,h,w]*.
  filters
      Convolution filters *[fd,fh,fw,d_out,d_in]*.
  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.
  output_shape
      Shape of the output (Default value = None)
  filter_format
      Either "channel_first" or "channel_last". "channel_first" corresponds
      to "IODHW",input data formats, while "channel_last" corresponds to "DHWOI".
  data_format
      The ordering of the dimensions in the input, one of "NDHWC" or
      "NCDHW". "NDHWC" corresponds to inputs with shape (batch_size,
       depth, height, width, channels), while "NCDHW" corresponds
       to input with shape (batch_size, channels, depth, height,
       width).
  dilations
      The dilation factor for each dimension of input. (Default value = 1)
  bias
      Bias array of shape *[d_out]*.
  out
      optional output container, for writing the result to. It must
      have a shape that the inputs broadcast to.
  
  Returns
  -------
  ret
      The result of the transpose convolution operation in a container.
  
  >>> a = ivy.random_normal(mean=0, std=1, shape=[1, 3, 14, 14, 3])
  >>> b = ivy.random_normal(mean=0, std=1, shape=[1, 3, 28, 28, 3]))
  >>> c = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 6, 3])
  >>> d = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 6, 3]))
  >>> x = ivy.Container(a=a, b=b)
  >>> filters = ivy.Container(c=c, d=d)
  >>> y = ivy.Container.static_conv3d_transpose(x, filters, 2, 'SAME')
  >>> print(y.shape)
  {
      a: {
          c: [1, 6, 28, 28, 6],
          d: [1, 6, 28, 28, 6]
      },
      b: {
          c: [1, 6, 56, 56, 6],
          d: [1, 6, 56, 56, 6]
      }
  }
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code: def _static_conv3d_transpose(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      filters: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      strides: Union[
          int,
          Tuple[int],
          Tuple[int, int],
          Tuple[int, int, int],
          ivy.Container,
      ],
      padding: Union[str, List[int], ivy.Container],
      /,
      *,
      output_shape: Optional[
          Union[ivy.Array, ivy.NativeArray, ivy.Container]
      ] = None,
      filter_format: str = "channel_last",
      data_format: str = "NDHWC",
      dilations: Union[
          int, Tuple[int], Tuple[int, 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 static method variant of ivy.conv3d_transpose. This
      method simply wraps the function, and so the docstring for
      ivy.conv3d_transpose also applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          Input container with leaves of volume *[batch_size,d,h,w,d_in]*
          or *[batch_size,d_in,d,h,w]*.
      filters
          Convolution filters *[fd,fh,fw,d_out,d_in]*.
      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.
      output_shape
          Shape of the output (Default value = None)
      filter_format
          Either "channel_first" or "channel_last". "channel_first" corresponds
          to "IODHW",input data formats, while "channel_last" corresponds to "DHWOI".
      data_format
          The ordering of the dimensions in the input, one of "NDHWC" or
          "NCDHW". "NDHWC" corresponds to inputs with shape (batch_size,
           depth, height, width, channels), while "NCDHW" corresponds
           to input with shape (batch_size, channels, depth, height,
           width).
      dilations
          The dilation factor for each dimension of input. (Default value = 1)
      bias
          Bias array of shape *[d_out]*.
      out
          optional output container, for writing the result to. It must
          have a shape that the inputs broadcast to.
  
      Returns
      -------
      ret
          The result of the transpose convolution operation in a container.
  
      >>> a = ivy.random_normal(mean=0, std=1, shape=[1, 3, 14, 14, 3])
      >>> b = ivy.random_normal(mean=0, std=1, shape=[1, 3, 28, 28, 3]))
      >>> c = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 6, 3])
      >>> d = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 6, 3]))
      >>> x = ivy.Container(a=a, b=b)
      >>> filters = ivy.Container(c=c, d=d)
      >>> y = ivy.Container.static_conv3d_transpose(x, filters, 2, 'SAME')
      >>> print(y.shape)
      {
          a: {
              c: [1, 6, 28, 28, 6],
              d: [1, 6, 28, 28, 6]
          },
          b: {
              c: [1, 6, 56, 56, 6],
              d: [1, 6, 56, 56, 6]
          }
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "conv3d_transpose",
          x,
          filters,
          strides,
          padding,
          output_shape=output_shape,
          filter_format=filter_format,
          data_format=data_format,
          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 27 for calling the _ContainerWithLayers obj's underlying member method code with example usage: obj._static_conv3d_transpose() and returns: ContainerBase