`compression` argument.

*   Keras:

    *   `tf.keras.layers.Conv` now includes a public `convolution_op` method.
        This method can be used to simplify the implementation of Conv
        subclasses. There are two primary ways to use this new method. The first
        is to use the method directly in your own `call` method: `python class

		{"(*Prog).StartCond", Method, 0},
		{"(*Prog).String", Method, 0},
		{"(*Regexp).CapNames", Method, 0},
		{"(*Regexp).Equal", Method, 0},
		{"(*Regexp).MaxCap", Method, 0},
		{"(*Regexp).Simplify", Method, 0},
		{"(*Regexp).String", Method, 0},
		{"(ErrorCode).String", Method, 0},
		{"(InstOp).String", Method, 3},
		{"(Op).String", Method, 11},
		{"ClassNL", Const, 0},
		{"Compile", Func, 0},

		})
		// note the unconditional `true` here for hostUsers. The failure case to test for ProcMount only includes it being true,
		// and the field is ignored if ProcMount isn't set. Thus, we can unconditionally set to `true` and simplify the test matrix setup.
		if errs := ValidateSecurityContext(v.sc, field.NewPath("field"), true); len(errs) == 0 || errs[0].Type != v.errorType || !strings.Contains(errs[0].Detail, v.errorDetail) {

&:not(:first-child) .custom-file-label { @include border-left-radius(0); }\n  }\n}\n\n\n// Prepend and append\n//\n// While it requires one extra layer of HTML for each, dedicated prepend and\n// append elements allow us to 1) be less clever, 2) simplify our selectors, and\n// 3) support HTML5 form validation.\n\n.input-group-prepend,\n.input-group-append {\n  display: flex;\n\n  // Ensure buttons are always above inputs for more visually pleasing borders.\n  // This isn't needed for `.input-group-text`...

&:not(:first-child) .custom-file-label { @include border-left-radius(0); }\n  }\n}\n\n\n// Prepend and append\n//\n// While it requires one extra layer of HTML for each, dedicated prepend and\n// append elements allow us to 1) be less clever, 2) simplify our selectors, and\n// 3) support HTML5 form validation.\n\n.input-group-prepend,\n.input-group-append {\n  display: flex;\n\n  // Ensure buttons are always above inputs for more visually pleasing borders.\n  // This isn't needed for `.input-group-text`...

The op uses LU decomposition with partial pivoting to compute the inverses.

If a matrix is not invertible there is no guarantee what the op does. It
may detect the condition and raise an exception or it may simply return a
garbage result.
  }];

  let arguments = (ins
    Arg<TensorOf<[TF_Complex128, TF_Complex64, TF_Float16, TF_Float32, TF_Float64]>, [{Shape is `[..., M, M]`.}]>:$input,

        "properties": {
          "command": {
            "description": "Command is the command line to execute inside the container, the working directory for the command  is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.",