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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def astype(self, col_dtypes, **kwargs):
 """Converts columns dtypes to given dtypes. Args: name and dtype is a numpy dtype. Returns: DataFrame with updated dtypes. """

        # Group indices to update by dtype for less map operations
        dtype_indices = {}
        columns = col_dtypes.keys()
        numeric_indices = list(self.columns.get_indexer_for(columns))
        # Create Series for the updated dtypes
        new_dtypes = self.dtypes.copy()
        for i, column in enumerate(columns):
            dtype = col_dtypes[column]
            if (
                not isinstance(dtype, type(self.dtypes[column]))
                or dtype != self.dtypes[column]
            ):
                # Only add dtype only if different
                if dtype in dtype_indices.keys():
                    dtype_indices[dtype].append(numeric_indices[i])
                else:
                    dtype_indices[dtype] = [numeric_indices[i]]
                # Update the new dtype series to the proper pandas dtype
                try:
                    new_dtype = np.dtype(dtype)
                except TypeError:
                    new_dtype = dtype
                if dtype != np.int32 and new_dtype == np.int32:
                    new_dtype = np.dtype("int64")
                elif dtype != np.float32 and new_dtype == np.float32:
                    new_dtype = np.dtype("float64")
                new_dtypes[column] = new_dtype
        # Update partitions for each dtype that is updated
        new_data = self.data
        for dtype in dtype_indices.keys():

            def astype(df, internal_indices=[]):
                block_dtypes = {}
                for ind in internal_indices:
                    block_dtypes[df.columns[ind]] = dtype
                return df.astype(block_dtypes)

            new_data = new_data.apply_func_to_select_indices(
                0, astype, dtype_indices[dtype], keep_remaining=True
            )
        return self.__constructor__(new_data, self.index, self.columns, new_dtypes)