docstr: ivy.Array instance method variant of ivy.conv1d_transpose. This
  method simply wraps the function, and so the docstring for
  ivy.conv1d_transpose also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Input image *[batch_size,w,d_in]* or *[batch_size,d_in,w]*.
  filters
      Convolution filters *[fw,d_out,d_in]*.
  strides
      The stride of the sliding window for each dimension of input.
  padding
      either the string ‘SAME’ (padding with zeros evenly), the string ‘VALID’ (no
      padding), or a sequence of n (low, high) integer pairs that give the padding
      to apply before and after each spatial dimension.
  output_shape
      Shape of the output (Default value = None)
  filter_format
      Either "channel_first" or "channel_last". "channel_first" corresponds
      to "IOW",input data formats, while "channel_last" corresponds to "WOI".
  data_format
      The ordering of the dimensions in the input, one of "NWC" or "NCW". "NWC"
      corresponds to input with shape (batch_size, width, channels), while "NCW"
      corresponds to input with shape (batch_size, channels, width).
  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.array([[[1., 2.], [3., 4.], [6., 7.], [9., 11.]]])  # NWC
  >>> filters = ivy.array([[[0., 1.], [1., 1.]]])  # WIO (I == C)
  >>> result = x.conv1d_transpose(filters, (1,), 'VALID')
  >>> print(result)
  ivy.array([[[ 2.,  3.],
  ...         [ 4.,  7.],
  ...         [ 7., 13.],
  ...         [11., 20.]]])
docstr_tok: ["ivy.Array","instance","method","variant","of","ivy.conv1d_transpose",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","ivy.conv1d_transpose","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","self","Input","image","*","[","batch_size",",","w",",","d_in","]","*","or","*","[","batch_size",",","d_in",",","w","]","*",".","filters","Convolution","filters","*","[","fw",",","d_out",",","d_in","]","*",".","strides","The","stride","of","the","sliding","window","for","each","dimension","of","input",".","padding","either","the","string","\u2018","SAME","\u2019","(","padding","with","zeros","evenly",")",",","the","string","\u2018","VALID","\u2019","(","no","padding",")",",","or","a","sequence","of","n","(","low",",","high",")","integer","pairs","that","give","the","padding","to","apply","before","and","after","each","spatial","dimension",".","output_shape","Shape","of","the","output","(","Default","value","=","None",")","filter_format","Either","``","channel_first","''","or","``","channel_last","''",".","``","channel_first","''","corresponds","to","``","IOW","''",",","input","data","formats",",","while","``","channel_last","''","corresponds","to","``","WOI","''",".","data_format","The","ordering","of","the","dimensions","in","the","input",",","one","of","``","NWC","''","or","``","NCW","''",".","``","NWC","''","corresponds","to","input","with","shape","(","batch_size",",","width",",","channels",")",",","while","``","NCW","''","corresponds","to","input","with","shape","(","batch_size",",","channels",",","width",")",".","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.array","(","[","[","[","1.",",","2",".","]",",","[","3.",",","4",".","]",",","[","6.",",","7",".","]",",","[","9.",",","11",".","]","]","]",")","#","NWC",">",">",">","filters","=","ivy.array","(","[","[","[","0.",",","1",".","]",",","[","1.",",","1",".","]","]","]",")","#","WIO","(","I","==","C",")",">",">",">","result","=","x.conv1d_transpose","(","filters",",","(","1",",",")",",","'VALID","'",")",">",">",">","print","(","result",")","ivy.array","(","[","[","[","2.",",","3",".","]",",","...","[","4.",",","7",".","]",",","...","[","7.",",","13",".","]",",","...","[","11.",",","20",".","]","]","]",")"]
code: def conv1d_transpose(
      self: ivy.Array,
      filters: Union[ivy.Array, ivy.NativeArray],
      strides: Union[int, Tuple[int]],
      padding: str,
      /,
      *,
      output_shape: Optional[Union[ivy.Shape, ivy.NativeShape]] = None,
      filter_format: str = "channel_last",
      data_format: str = "NWC",
      dilations: Union[int, Tuple[int]] = 1,
      bias: Optional[ivy.Array] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.conv1d_transpose. This
      method simply wraps the function, and so the docstring for
      ivy.conv1d_transpose also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Input image *[batch_size,w,d_in]* or *[batch_size,d_in,w]*.
      filters
          Convolution filters *[fw,d_out,d_in]*.
      strides
          The stride of the sliding window for each dimension of input.
      padding
          either the string ‘SAME’ (padding with zeros evenly), the string ‘VALID’ (no
          padding), or a sequence of n (low, high) integer pairs that give the padding
          to apply before and after each spatial dimension.
      output_shape
          Shape of the output (Default value = None)
      filter_format
          Either "channel_first" or "channel_last". "channel_first" corresponds
          to "IOW",input data formats, while "channel_last" corresponds to "WOI".
      data_format
          The ordering of the dimensions in the input, one of "NWC" or "NCW". "NWC"
          corresponds to input with shape (batch_size, width, channels), while "NCW"
          corresponds to input with shape (batch_size, channels, width).
      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.array([[[1., 2.], [3., 4.], [6., 7.], [9., 11.]]])  # NWC
      >>> filters = ivy.array([[[0., 1.], [1., 1.]]])  # WIO (I == C)
      >>> result = x.conv1d_transpose(filters, (1,), 'VALID')
      >>> print(result)
      ivy.array([[[ 2.,  3.],
      ...         [ 4.,  7.],
      ...         [ 7., 13.],
      ...         [11., 20.]]])
      """
      return ivy.conv1d_transpose(
          self._data,
          filters,
          strides,
          padding,
          output_shape=output_shape,
          filter_format=filter_format,
          data_format=data_format,
          dilations=dilations,
          bias=bias,
          out=out,
      )
  
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 9 for calling the _ArrayWithLayers obj's underlying member method code with example usage: obj.conv1d_transpose() and returns: ivy