(self, dim0, x=None, dtype=None, name=None, persist_values=True,
              shared=None, parallelism=None)
return out_tsr
Allocate input and output buffer for layer based on batch size. This
        is used because the layer does not know about the batch size.

        Arguments:
            dim0 (tuple or int): I/O buffer dimension for layer (without the
                                 axis specifying the batch size).
            x (data-type, optional): If present and not None, `x` will be
                                     returned directly. `x` will be not None if
                                     the buffer has already been allocated.
            dtype (data-type, optional): If present, specifies the underlying
                                         type to employ for each element.
            name (str, optional): name indentifying the tensor (used in printing).
            persist_values (bool, optional): If set to True (the default), the
                                             values assigned to this Tensor will
                                             persist across multiple begin and
                                             end calls.  Setting to False may
                                             provide a performance increase if
                                             values do not need to be maintained
                                             across such calls
            shared (buffer, optional): If present will attempt to reuse the memory
                                       in shared to allocate the I/O buffer
            parallelism (str, optional): Indicates type of parallelism (Data,
                                         Model) employed by this buffer.
                                         Ignored on CPU and GPU backends,
                                         defaults to no parallelism.
        Returns:
            Tensor: array object
Allocate input and output buffer for layer based on batch size. This
        is used because the layer does not know about the batch size.
Allocate input and output buffer for layer based on batch size . This is used because the layer does not know about the batch size .
def iobuf(self, dim0, x=None, dtype=None, name=None, persist_values=True,
              shared=None, parallelism=None):
        """
        Allocate input and output buffer for layer based on batch size. This
        is used because the layer does not know about the batch size.

        Arguments:
            dim0 (tuple or int): I/O buffer dimension for layer (without the
                                 axis specifying the batch size).
            x (data-type, optional): If present and not None, `x` will be
                                     returned directly. `x` will be not None if
                                     the buffer has already been allocated.
            dtype (data-type, optional): If present, specifies the underlying
                                         type to employ for each element.
            name (str, optional): name indentifying the tensor (used in printing).
            persist_values (bool, optional): If set to True (the default), the
                                             values assigned to this Tensor will
                                             persist across multiple begin and
                                             end calls.  Setting to False may
                                             provide a performance increase if
                                             values do not need to be maintained
                                             across such calls
            shared (buffer, optional): If present will attempt to reuse the memory
                                       in shared to allocate the I/O buffer
            parallelism (str, optional): Indicates type of parallelism (Data,
                                         Model) employed by this buffer.
                                         Ignored on CPU and GPU backends,
                                         defaults to no parallelism.
        Returns:
            Tensor: array object
        """
        if x is not None:
            return x
        if isinstance(dim0, tuple):
            if (len(dim0) == 2):
                bufshape = (dim0[0], dim0[1] * self.bsz)
            else:
                bufshape = (int(np.prod(dim0)), self.bsz)
        else:
            bufshape = (dim0, self.bsz)

        if shared is not None:
            out_tsr = shared if shared.shape == bufshape else shared.share(bufshape)
        else:
            out_tsr = self.empty(bufshape, dtype=dtype, name=name, persist_values=persist_values)

        out_tsr[:] = 0

        return out_tsr
def iobuf ( self , dim0 , x = None , dtype = None , name = None , persist_values = True , shared = None , parallelism = None ) : if x is not None : return x if isinstance ( dim0 , tuple ) : if ( len ( dim0 ) == 2 ) : bufshape = ( dim0 [ 0 ] , dim0 [ 1 ] * self . bsz ) else : bufshape = ( int ( np . prod ( dim0 ) ) , self . bsz ) else : bufshape = ( dim0 , self . bsz ) if shared is not None : out_tsr = shared if shared . shape == bufshape else shared . share ( bufshape ) else : out_tsr = self . empty ( bufshape , dtype = dtype , name = name , persist_values = persist_values ) out_tsr [ : ] = 0 return out_tsr