docstr: ivy.Array instance method variant of `ivy.conv2d_transpose`. This
  method simply wraps the function, and so the docstring for
  `ivy.conv2d_transpose` also applies to this method with minimal
  changes.
  
  Parameters
  ----------
  self
      Input image *[batch_size,h,w,d_in]* or *[batch_size,d_in,h,w]*.
  filters
      Convolution filters *[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 "IOHW",input data formats, while "channel_last" corresponds to "HWOI".
  data_format
      The ordering of the dimensions in the input, one of "NHWC" or "NCHW". "NHWC"
      corresponds to inputs with shape (batch_size, height, width, channels),
      while "NCHW" corresponds to input with shape (batch_size, channels, height,
      width). Default is ``"NHWC"``.
  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 transpose convolution operation.
  
  Examples
  --------
  >>> x = ivy.random_normal(mean=0, std=1, shape=[1, 28, 28, 3])
  >>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 6, 3])
  >>> y = x.conv2d_transpose(filters,2,'SAME',)
  >>> print(y.shape)
  (1, 56, 56, 6)
docstr_tok: ["ivy.Array","instance","method","variant","of","`","ivy.conv2d_transpose","`",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","`","ivy.conv2d_transpose","`","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","self","Input","image","*","[","batch_size",",","h",",","w",",","d_in","]","*","or","*","[","batch_size",",","d_in",",","h",",","w","]","*",".","filters","Convolution","filters","*","[","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","``","IOHW","''",",","input","data","formats",",","while","``","channel_last","''","corresponds","to","``","HWOI","''",".","data_format","The","ordering","of","the","dimensions","in","the","input",",","one","of","``","NHWC","''","or","``","NCHW","''",".","``","NHWC","''","corresponds","to","inputs","with","shape","(","batch_size",",","height",",","width",",","channels",")",",","while","``","NCHW","''","corresponds","to","input","with","shape","(","batch_size",",","channels",",","height",",","width",")",".","Default","is","``","``","NHWC","''","``",".","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","transpose","convolution","operation",".","Examples","--","--","--","--",">",">",">","x","=","ivy.random_normal","(","mean=0",",","std=1",",","shape=","[","1",",","28",",","28",",","3","]",")",">",">",">","filters","=","ivy.random_normal","(","mean=0",",","std=1",",","shape=","[","3",",","3",",","6",",","3","]",")",">",">",">","y","=","x.conv2d_transpose","(","filters,2",",","'SAME","'",",",")",">",">",">","print","(","y.shape",")","(","1",",","56",",","56",",","6",")"]
code: def conv2d_transpose(
      self: ivy.Array,
      filters: Union[ivy.Array, ivy.NativeArray],
      strides: Union[int, Tuple[int, int]],
      padding: str,
      /,
      *,
      output_shape: Optional[Union[ivy.Shape, ivy.NativeShape]] = None,
      filter_format: str = "channel_last",
      data_format: str = "NHWC",
      dilations: Union[int, Tuple[int, int]] = 1,
      out: Optional[ivy.Array] = None,
      bias: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of `ivy.conv2d_transpose`. This
      method simply wraps the function, and so the docstring for
      `ivy.conv2d_transpose` also applies to this method with minimal
      changes.
  
      Parameters
      ----------
      self
          Input image *[batch_size,h,w,d_in]* or *[batch_size,d_in,h,w]*.
      filters
          Convolution filters *[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 "IOHW",input data formats, while "channel_last" corresponds to "HWOI".
      data_format
          The ordering of the dimensions in the input, one of "NHWC" or "NCHW". "NHWC"
          corresponds to inputs with shape (batch_size, height, width, channels),
          while "NCHW" corresponds to input with shape (batch_size, channels, height,
          width). Default is ``"NHWC"``.
      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 transpose convolution operation.
  
      Examples
      --------
      >>> x = ivy.random_normal(mean=0, std=1, shape=[1, 28, 28, 3])
      >>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 6, 3])
      >>> y = x.conv2d_transpose(filters,2,'SAME',)
      >>> print(y.shape)
      (1, 56, 56, 6)
      """
      return ivy.conv2d_transpose(
          self._data,
          filters,
          strides,
          padding,
          output_shape=output_shape,
          filter_format=filter_format,
          data_format=data_format,
          dilations=dilations,
          out=out,
          bias=bias,
      )
  
file_path: ivy/ivy/data_classes/array/layers.py
imports: import abc
  from typing import Optional, Tuple, Union, List, Sequence
  import ivy
label_desc: Use image node_id 12 for calling the _ArrayWithLayers obj's underlying member method code with example usage: obj.conv2d_transpose() and returns: ivy