code: def conv_general_transpose(
      x: Union[ivy.Array, ivy.NativeArray],
      filters: Union[ivy.Array, ivy.NativeArray],
      strides: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ],
      padding: str,
      /,
      *,
      dims: int = 2,
      output_shape: Optional[Union[ivy.Shape, ivy.NativeShape]] = None,
      filter_format: str = "channel_last",
      data_format: str = "channel_last",
      dilations: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ] = 1,
      feature_group_count: int = 1,
      bias: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Compute a 1-D, 2-D, and 3-D transpose convolution given 3-D, 4-D and 5-D
      input x respectively and filters arrays.
  
      Parameters
      ----------
      x
          Input image *[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
          Either ‘SAME’ (padding so that the output's shape is the same as the
          input's), or ‘VALID’ (padding so that the output's shape is `output_shape`).
      dims
          Either 1, 2, or 3 corresponding to 1-D, 2-D, and 3-D convolution.
      output_shape
          Shape of the output.
      filter_format
          Either "channel_first" or "channel_last". "channel_first" corresponds
          to "IODHW",input data formats, while "channel_last" corresponds to "DHWOI".
      data_format
          Either "channel_first" or "channel_last". "channel_first" corresponds to "NCW",
          "NCHW", "NCDHW" input data formatS for 1-D, 2-D, 3-D convolution respectively,
          while "channel_last" corresponds to "NWC", "NHWC", "NDHWC" respectively.
      dilations
          The dilation factor for each dimension of input. (Default value = 1)
      feature_group_count
           split input into groups, d_in should be divisible by the number of groups.
      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 transpose convolution operation.
  
      Examples
      --------
      With :class:`ivy.Array` input:
      >>> x = ivy.random_normal(mean=0, std=1, shape=[1, 3, 28, 28, 3])
      >>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 6, 3])
      >>> y = ivy.conv3d_transpose(x, filters, [2, 2, 2], 'SAME')
      >>> print(y.shape)
      ivy.Shape(1, 6, 56, 56, 6)
      >>> x = ivy.random_normal(mean=0, std=1, shape=[1, 3, 64, 64, 3])
      >>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 6, 3])
      >>> y = ivy.conv3d_transpose(x, filters, [2, 2, 2], 'VALID', dilations=[1, 1, 1])
      >>> print(y.shape)
      ivy.Shape(1, 7, 129, 129, 6)
      With :class: 'ivy.Container' inputs:
      >>> 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=[6, 3, 3, 3, 3])
      >>> d = ivy.random_normal(mean=0, std=1, shape=[6, 3, 3, 3, 3])
      >>> x = ivy.Container(a=a, b=b)
      >>> filters = ivy.Container(c=c, d=d)
      >>> y = ivy.conv3d_transpose(x, filters, [2, 2, 2], 'SAME')
      >>> print(y.shape)
      {
          a: {
              c: ivy.Shape(1, 6, 28, 28, 3),
              d: ivy.Shape(1, 6, 28, 28, 3)
          },
          b: {
              c: ivy.Shape(1, 6, 56, 56, 3),
              d: ivy.Shape(1, 6, 56, 56, 3)
          },
          c: {
              c: ivy.Shape(6, 6, 6, 6, 3),
              d: ivy.Shape(6, 6, 6, 6, 3)
          },
          d: {
              c: ivy.Shape(6, 6, 6, 6, 3),
              d: ivy.Shape(6, 6, 6, 6, 3)
          }
      }
      With a mix of :class:`ivy.Array` and :class:`ivy.Container` inputs:
      >>> x = ivy.full((1, 6, 6, 6, 1), 2.7)
      >>> a = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 1, 1])
      >>> b = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 1, 1])
      >>> filters = ivy.Container(a=a, b=b)
      >>> y = ivy.conv3d_transpose(x, filters, [1, 1, 1], 'VALID', dilations=[1, 1, 1])
      >>> print(y.shape)
      {
          a: ivy.Shape(1, 8, 8, 8, 1),
          b: ivy.Shape(1, 8, 8, 8, 1)
      }
      >>> x = ivy.full((1, 6, 6, 6, 1), 1.23)
      >>> a = ivy.array(ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 1, 1]))
      >>> b = ivy.array(ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 1, 1]))
      >>> filters = ivy.Container(a=a, b=b)
      >>> y = ivy.conv3d_transpose(x, filters, [1, 1, 1], 'VALID', dilations=[1, 1, 1])
      >>> print(y.shape)
      {
          a: ivy.Shape(1, 8, 8, 8, 1),
          b: ivy.Shape(1, 8, 8, 8, 1)
      }
      """
      return current_backend(x).conv_general_transpose(
          x,
          filters,
          strides,
          padding,
          dims=dims,
          output_shape=output_shape,
          filter_format=filter_format,
          data_format=data_format,
          dilations=dilations,
          feature_group_count=feature_group_count,
          bias=bias,
          out=out,
      )
  
imports: from typing import Optional, Tuple, Union, Sequence
  import ivy
  from ivy.utils.backend import current_backend
  from ivy.func_wrapper import handle_array_function, handle_partial_mixed_function, inputs_to_ivy_arrays, to_native_arrays_and_back, inputs_to_native_shapes, handle_out_argument, handle_nestable, handle_array_like_without_promotion, handle_device, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 15 for calling a global function with example usage: conv_general_transpose() and returns: current_backend