parameters: (inputs,
                   kernel,
                   kernel_size,
                   strides,
                   output_shape,
                   data_format=None)
return_statement: return local_conv(inputs,
                      kernel,
                      kernel_size,
                      strides,
                      output_shape,
                      data_format)
docstring: Apply 2D conv with un-shared weights.
  
    Arguments:
        inputs: 4D tensor with shape:
            (batch_size, filters, new_rows, new_cols)
            if data_format='channels_first'
            or 4D tensor with shape:
            (batch_size, new_rows, new_cols, filters)
            if data_format='channels_last'.
        kernel: the unshared weight for convolution,
            with shape (output_items, feature_dim, filters).
        kernel_size: a tuple of 2 integers, specifying the
            width and height of the 2D convolution window.
        strides: a tuple of 2 integers, specifying the strides
            of the convolution along the width and height.
        output_shape: a tuple with (output_row, output_col).
        data_format: the data format, channels_first or channels_last.
  
    Returns:
        A 4D tensor with shape:
        (batch_size, filters, new_rows, new_cols)
        if data_format='channels_first'
        or 4D tensor with shape:
        (batch_size, new_rows, new_cols, filters)
        if data_format='channels_last'.
docstring_summary: Apply 2D conv with un-shared weights.
docstring_tokens: Apply 2D conv with un - shared weights .
function: def local_conv2d(inputs,
                   kernel,
                   kernel_size,
                   strides,
                   output_shape,
                   data_format=None):
    """Apply 2D conv with un-shared weights.
  
    Arguments:
        inputs: 4D tensor with shape:
            (batch_size, filters, new_rows, new_cols)
            if data_format='channels_first'
            or 4D tensor with shape:
            (batch_size, new_rows, new_cols, filters)
            if data_format='channels_last'.
        kernel: the unshared weight for convolution,
            with shape (output_items, feature_dim, filters).
        kernel_size: a tuple of 2 integers, specifying the
            width and height of the 2D convolution window.
        strides: a tuple of 2 integers, specifying the strides
            of the convolution along the width and height.
        output_shape: a tuple with (output_row, output_col).
        data_format: the data format, channels_first or channels_last.
  
    Returns:
        A 4D tensor with shape:
        (batch_size, filters, new_rows, new_cols)
        if data_format='channels_first'
        or 4D tensor with shape:
        (batch_size, new_rows, new_cols, filters)
        if data_format='channels_last'.
    """
    return local_conv(inputs,
                      kernel,
                      kernel_size,
                      strides,
                      output_shape,
                      data_format)
function_tokens: def local_conv2d ( inputs , kernel , kernel_size , strides , output_shape , data_format = None ) : return local_conv ( inputs , kernel , kernel_size , strides , output_shape , data_format )